Code
knitr::include_graphics("figure1.png")
1 Executive Summary
Modern biomedical research generates vast volumes of genomic data, yet for many students, especially those at the start of their research journey—the path from raw gene expression profiles to clinically actionable insights remains unclear. While theoretical frameworks are covered in coursework, few resources offer guided, hands-on exposure to real-world biomedical datasets. Preva addresses this gap by providing an interactive EdTech tool designed specifically for Honours-level students entering the field of biomedical data science or precision medicine.
Developed using R Shiny, Preva walks users through the complete data science workflow: from understanding the biological foundations of breast cancer to pre-processing high-dimensional gene expression data and building classification models. It introduces students to key concepts in cancer pathology and gene regulation before guiding them through practical techniques such as data transformation, variable selection, and model development using algorithms like support vector machines (SVM), k-nearest neighbours (KNN), and random forests (RF).
The tool uses real-world datasets labeled by tumor grade, an ordinal clinical variable often used to assess disease severity and progression. This structure allows students to build and evaluate models aligned with how risk is communicated in clinical settings. Robust performance metrics such as balanced accuracy, F1-score, Kappa, and specificity help students assess their models in the face of class imbalance, a common issue in medical data.
Importantly, Preva was designed with accessibility in mind. It assumes minimal coding experience and is tailored for students who are curious about how genomics intersects with machine learning, but may lack confidence in programming or statistical modeling. Through interactive visualisations, scaffolded guidance, quizzes, and reflective prompts, students gain not only technical skills but also critical thinking abilities around model interpretability and translational challenges.
By offering an approachable yet rigorous learning experience, Preva transforms passive instruction into active discovery. It is a modular, scalable resource that enables Honours students to develop foundational competencies in biomedical modeling—and empowers them to take their first steps into the world of data-driven healthcare.
2 Background & Context
Breast cancer is the most commonly diagnosed cancer among Australian women, with over 21,000 new cases projected in 2024—approximately 1 in 7 women will be diagnosed in their lifetime (National Breast Cancer Foundation, 2024). While most diagnoses occur in women over 50, more than 1,000 cases were reported among women under 40 in 2023 (Breast Cancer Network Australia, 2023). Early detection remains a key factor in survival: when diagnosed at a localized stage, the five-year survival rate is 99%, compared to just 27% when cancer has spread (Medshun, 2023).
Advances in genomics and high-throughput technologies, such as microarrays and RNA sequencing, have opened new frontiers in disease prediction by enabling researchers to profile gene expression on a molecular level. These datasets are high-dimensional, often containing thousands of variables and only a limited number of samples. While statistically challenging, they hold tremendous potential to uncover biological signals that traditional imaging or self-reported data may overlook. Predictive models trained on gene expression data could help flag risk long before physical symptoms appear—providing powerful support for early intervention and personalized care.
Yet despite its promise, the field of genomic modeling remains inaccessible to many students, especially those who are new to coding or unsure how to apply data science concepts to real-world biological problems. This disconnect is particularly pronounced at the Honours level, where students often encounter complex datasets for the first time without sufficient scaffolding.
Preva was created to serve as a first point of contact for these students—offering an approachable, guided introduction to the practice of building cancer risk models using real gene expression data. It challenges students to grapple with the inherent messiness of biomedical data while introducing best practices like data transformation, variable selection with limma, and modeling for ordinal outcomes.
By simulating the full data science workflow in an educational setting, Preva empowers students to engage deeply with the iterative and interpretive aspects of biomedical modeling. Designed for senior undergraduates and early Honours students in biomedical or data science programs, Preva equips learners with the foundational skills and confidence they need to bridge the gap between theory and practice, and to make meaningful contributions to data-driven healthcare.
3 Methods
3.1 Obtaining data
Data was sourced using two primary methods: directly from the NCBI GEO database and through targeted searches using Google and Google Scholar. On the NCBI website, we explored the GEO Datasets portal using the keywords “breast cancer,” filtered by study type “Expression profiling by array” and organism “Homo sapiens.” Additionally, we performed broader web searches using terms such as “breast cancer dataset” to discover relevant studies hosted on GEO. This approach led us to three datasets: GSE15852, which was identified through the NCBI search, and GSE17907 and GSE10810, both of which were found via Google. The latter two were later merged to form a combined dataset.
3.2 EDA
After loading the data we explored the phenotype and expression data. We determined which columns represented the cancer grade, the distribution of grades in each dataset and whether there were NA values. Principal Component Analysis (PCA) was used to introduce students to unsupervised exploratory analysis and visualisation.
In the pipeline, PCA was applied to:
Detect and visualise sample clusters by grade or dataset
Identify batch effects and the need for log transformation (e.g., GSE15852
Plots from the PCA and other portions of EDA feature in Preva to assist the learning process.
3.3 Data Processing
We selected GSE15852, GSE17907, and a merged dataset (GSE17907 & GSE10810) for use in Preva. GSE10810 was excluded as a standalone dataset due to its low number of Grade 1 samples, which limited predictive performance and added little value.
Each selected dataset was imported using the GEOquery package, and the phenotype data was cleaned and standardized—particularly the grade labels—to ensure consistency, especially for merging datasets. Cleaned phenotype and gene expression data were then extracted. From the phenotype data, only sample name, grade, and source dataset were retained to form a simplified “metadata” file, while the expression data was compiled into a “merged_expr” dataset.
We removed samples without grade labels, as they offered no predictive value, and genes with NA values, due to incomplete expression profiles. Lowly expressed genes were filtered out to reduce noise, improve statistical power, and retain biological relevance.
Processing varied slightly across datasets. For GSE15852, a log(x + 1) transformation was applied after PCA analysis showed the data was not already log-transformed, unlike GSE17907 and GSE10810. For the combined dataset, batch correction was performed to account for technical variation introduced by merging studies, helping isolate true biological signals.
All datasets were then split into 80% training and 20% testing sets. The testing sets were kept separate for final validation, while training sets were used for model development. SMOTE re-sampling was applied only to the combined dataset to synthetically increase the number of Grade 1 samples and balance the class distribution. It was not used on GSE15852 or GSE17907 alone, as their small Grade 1 sample sizes could lead to unreliable synthetic data or over-fitting.
These dataset-specific pre-processing steps ensured that each input was appropriately cleaned, balanced, and ready for robust modeling within Preva.
Code
knitr::include_graphics("figure2.png")
3.3.1 Feature Selection
To introduce students to real-world feature selection, we used the limma package to perform differential expression analysis, retaining genes with an adjusted p-value < 0.05. From these, we created input sets of the top 30, 50, 80, and 110 genes for model training. This allows students to observe how feature count impacts model performance and prompts discussion on biological relevance.
3.4 Modelling/Analysis Approach
After performing feature selection, we evaluated each top gene using three different machine learning algorithms, resulting in twelve distinct model outcomes. For each model, we calculated key performance metrics including accuracy, balanced accuracy, recall, precision, F1 score, sensitivity, and specificity. These metrics were then visualized and compared, with the resulting plots integrated into the Shiny app for interactive exploration. The models used were:
3.4.1 Random Forest (RF)
RF is an ensemble method that builds multiple decision trees on random data and feature subsets, combining their outputs via majority voting. It handles high-dimensional gene expression data well and is robust to noise and over-fitting; key for Preva’s small datasets.
3.4.2 Support Vector Machine (SVM)
SVM finds the optimal hyperplane to separate classes in high-dimensional space. Using kernel tricks like the RBF kernel, it’s able to handle the non-linear relationships common in biological data and performs well with high-dimensional data.
3.4.3 K-Nearest Neighbours (KNN)
KNN is a simple, non-parametric algorithm that classifies based on the majority class among the k closest data points (using Euclidean distance). Its simplicity and interpretability make it ideal for teaching foundational ML concepts and benchmarking performance.
3.5 Shiny App
The Shiny app provides an interactive, browser-based learning experience that makes complex predictive genomics concepts accessible without coding. It is structured into four modules: Module 1 introduces breast cancer and gene expression data; Module 2 covers data processing; Module 3 focuses on model building; and Module 4 guides students through model evaluation. Each module includes clear explanations and a quiz to reinforce key data science concepts related to predictive modeling.
3.5.1 Educational Foundations of Preva
Learner-Centered Design
- Preva features intuitive interfaces, scaffolded tutorials, and low-pressure environments to support beginners, improving engagement and understanding (eLearning Industry, 2024).
Clear Learning Alignment
- Each module links to assessable learning outcomes, helping students understand the purpose behind each task and boosting knowledge retention (Instructure, n.d.; University of Hawaii, n.d.).
Interactive, Hands-On Learning
- By using real breast cancer gene expression data, customizable models, and visualizations, Preva promotes active learning and engagement (BMC Medical Education, 2024).
Best-Practice Pedagogy
- Preva integrates problem-based learning, reflection, and data storytelling to foster critical thinking and interdisciplinary literacy (ScienceDirect, 2024).
4 Result
4.1 Key Findings
In this study, we systematically evaluated the predictive performance of three machine learning models (SVM, KNN, and RF) on breast cancer grade classification across three datasets (Combined, GSE15852, and GSE17907) and four top-ranked gene sets (30, 50, 80, and 110 genes). The boxplots provide a clear visual comparison of the distribution of Balanced Accuracy and Macro F1 scores for each model across the different datasets.
From the boxplots, it is evident that the Combined dataset consistently outperformed the two individual datasets (GSE15852 and GSE17907) in terms of both Balanced Accuracy and Macro F1. The higher median values of the Combined dataset highlight its overall superior performance.
Focusing on the Combined dataset, the RF model demonstrated the most robust performance across different top gene sets. Notably, when using the top 80 genes, the RF model achieved a Balanced Accuracy of 0.673 and a Macro F1 score of 0.642, showcasing its strong predictive ability for breast cancer grade classification. The SVM and KNN models also exhibited stable performance on the Combined dataset, with Balanced Accuracy values generally exceeding 0.6 across most top-ranked gene sets, indicating good generalization capabilities.
Overall, the Combined dataset provided richer and more comprehensive biological signals, enabling all models—especially the RF classifier—to better differentiate between breast cancer grades.
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load("report_plot.RData")
boxplot_plot
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knitr::include_graphics("figure4.png")
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gene_heat_drawn
4.2 Interpretation
The results show clear patterns that make sense in a biomedical and data science context. Most notably, the combined dataset consistently performed better across all models. This reflects how integrating datasets can help reduce study-specific bias and make predictions more generalisable—something students can directly observe using Preva.
While RF achieved the best overall scores, it’s not about choosing one “winner.” One important learning point is that different models behave differently across datasets and feature sets. In some cases, SVM actually outperformed RF on GSE15852. KNN struggled with larger gene sets, likely due to its sensitivity to noisy or irrelevant features. These outcomes reflect common challenges in high-dimensional data analysis.
Preva is designed to help students explore these trade-offs for themselves. In Module 4, users can change parameters—like gene set size or model type—and immediately see how performance metrics shift. This interactivity turns abstract ideas into practical knowledge. Instead of just reading that more genes can improve performance “up to a point,” students can test that directly and notice how the gains flatten beyond a certain number of features.
Another key concept Preva helps communicate is the importance of using the right metrics. Accuracy, while intuitive, can be misleading when data is imbalanced. Balanced Accuracy, Macro-F1, and Specificity provide a clearer picture; especially when working with rare but clinically important cases like Grade 1 or Grade 3 tumors. The app doesn’t just display these values; it explains them, so students can learn why each one matters.
Beyond the numbers, Preva also guides students through the full modeling process, from biological context to technical evaluation. The modular layout helps scaffold their learning—Module 1 introduces breast cancer and gene expression, Module 2 handles preprocessing, and so on. Each stage builds on the last, supported by text explanations, quizzes, and visual feedback.
From an EdTech design perspective of the Shiny, Preva integrates multiple best practices. The app is interactive, modular, and explanatory, aligning with guidelines for effective digital learning tools (Backpack Interactive, n.d.; BMC Medical Education, 2024). Instructional design literature stresses the importance of learner agency and feedback, both of which Preva offers through parameter tuning and real-time metric updates (Instructure, n.d.; eLearning Industry, n.d.). Moreover, Preva’s integration of biomedical context ensures the learning remains applied and relevant, a strategy shown to deepen engagement and improve knowledge retention (ScienceDirect, 2021; University of Hawaii, n.d.).
Ultimately, Preva achieves its educational goal: to teach students how to build, evaluate, and interpret predictive models in a high-dimensional biomedical context. By combining domain knowledge, data science, and instructional design, it offers a cohesive learning experience that promotes exploration, critical thinking, and real-world applicability.
5 Discussion and Conclusion
This project set out to create an interactive educational tool to guide students through the process of building a breast cancer risk classifier using gene expression data. Our findings demonstrate that, while the models developed are moderately robust and primarily tailored to the datasets used, they offer meaningful insights within an educational context. Their performance was consistent across three separate datasets, various train/test splits, and unseen data, suggesting reasonable generalisability. For an application intended to teach foundational concepts, achieving over 50% accuracy in a multi-class setting is contextually acceptable.
Several limitations, however, constrain the reliability and interpretability of the current models. First, the datasets exhibit class imbalance and uneven sample sizes. While SMOTE was applied to oversample minority classes, synthetic data cannot fully replicate the complexity of real biological samples. Second, the feature selection relied solely on the limma method, which is based on linear modelling assumptions. Given that gene–grade relationships may be nonlinear, this could limit the biological accuracy of selected features. Lastly, while models such as SVM, KNN, and Random Forest performed well in classification tasks, they lack transparency in terms of how individual genes contribute to predictions, which reduces interpretability for learners aiming to understand underlying biological mechanisms.
To further improve Preva, both technically and educationally, we propose the following enhancements:
Train models on larger and more diverse datasets, particularly those with matched normal controls, to improve generalisability and biological relevance.
Enable user-adjustable model parameters (e.g., k in KNN, number of trees in RF) to promote hands-on experimentation and deeper learning.
Support datasets from other cancer types in the same format, allowing broader application of Preva across various genomic contexts.
In summary, Preva is an effective and accessible educational tool that lowers the barrier to understanding genomics and predictive modelling in disease contexts. It introduces honours-level biomedical students to the role of gene expression in breast cancer classification, bridging the gap between theory and application. With its flexibility, intuitive interface, and potential for expansion, Preva offers a valuable platform for teaching the practical and conceptual foundations of genomic data science.
6 Student Contributions
Devanshi
Coordinated and led weekly meetings, set up GitHub, Google Docs, and communication channels, and maintained meeting minutes. Sourced GSE15852, contributed to EDA and early model building, and shaped the report and presentation structure. Wrote the Executive Summary, Background, part of the Method, and designed Figure 1.
Hamish
Developed a quick, simple, shiny app (Not the extensive Shiny app submitted), created quiz Questions, wrote “About” page content, wrote module 1 content, contributed to LO7: Communicating Findings content. “Limitations” and “Future directions”. Also wrote discussion and conclusion in the report.
Tommi
Sourced GSE17907 and GSE10810, creating a merged dataset from these two performing batch correction and resampling. Contributed the EDA, model building, created visualisations for data processing and final metric summary. I also wrote the methods section of the report and created figures 2 and 5.
Harry
Responsible for EDA, data pre-processing, model building, and visualisations. Later, assisted Dorothea in upgrading shiny, which included the analysis of the model and the data processing part. Wrote part A of the result section in the report, as well as the data introduction, limitations and data processing in shiny.
Dorothea
I designed and implemented the Shiny app’s UI, logo, selectors, styling, and all instructional content across tabs. I developed key modules including Gene Analysis, Performance, and Key Findings Summary, created the Quarto report template to match the app’s theme, wrote Results Part B, and briefly explored XGBoost during early modelling.
7 References
Australian Institute of Health and Welfare. (2024). Cancer data in Australia. Retrieved from
https://www.aihw.gov.au/reports/cancer/cancer-data-in-australia/contents/overview
Backpack Interactive. (n.d.). Interactive educational technology for better learning outcomes.
https://backpackinteractive.com/insights/interactive-educational-technology
BMC Medical Education. (2024). Digital learning of clinical skills and its impact on medical students. https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-024-06471-2
Breast Cancer Network Australia. (2024). Breast statistics cancer in Australia. Retrieved from
https://www.bcna.org.au/resources/about-breast-cancer/breast-statistics-cancer-in-australia/
eLearning Industry. (n.d.). Elevating education through instructional design: Principles and key components.
https://elearningindustry.com/elevating-education-through-instructional-design-principles-and-key-components
ERIC. (2024). Research-based and user-centered development of EdTech tools.
https://files.eric.ed.gov/fulltext/ED649967.pdf
Instructure. (n.d.). Key principles of instructional design: How to craft effective learning experiences.
https://www.instructure.com/resources/blog/key-principles-instructional-design-how-craft-effective-learning-experiences
Medshun. (n.d.). The importance of early detection: Key to successful breast cancer treatment. Retrieved from https://medshun.com/article/finding-breast-cancer-early-is-the-key-to-successful-treatment
National Breast Cancer Foundation. (2024). Breast cancer statistics in Australia. Retrieved from
https://nbcf.org.au/about-breast-cancer/breast-cancer-stats/
ScienceDirect. (2021). The use of educational technology for interactive teaching in lectures. https://www.sciencedirect.com/science/article/pii/S2049080121000522
ScienceDirect. (2024). Learning to teach: Aligning pedagogy and technology in a learning design tool.
https://www.sciencedirect.com/science/article/pii/S0742051X24002257
University of Hawaii. (n.d.). The design of effective ICT-supported learning activities: Exemplary models, changing requirements, and new possibilities.
https://scholarspace.manoa.hawaii.edu/bitstream/10125/44115/1/1269.pdf
UpGuage. (n.d.). Best practices for educational institutions using EdTech.
https://www.upguage.com/edtech_best_practices
8 Appendix
8.1 Appendix A: Code for data processing, model building and evaluation with visualisations for all 3 datasets.
8.1.1 combined code
Code
# Function to extract and standardize grade into correct names
standardize_grade <- function(grades, pattern, replacements, remove_na = TRUE) {
grades <- tolower(grades)
for (i in seq_along(pattern)) {
grades <- gsub(pattern[i], replacements[i], grades)
}
grades <- gsub(".*(\\b[0-3]\\b).*", "\\1", grades) # Extract grade number if embedded
grades[!grades %in% c("0", "1", "2", "3")] <- NA # Drop anything unexpected
if (remove_na) {
grades <- as.numeric(grades)
}
# Map grades to full names
grade_names <- c("0" = "Normal", "1" = "Grade1", "2" = "Grade2", "3" = "Grade3")
grades <- grade_names[as.character(grades)] # Map numbers to grade names
return(grades)
}
### GSE10810
pdata10810 <- pData(gse10810)
grades10810 <- pdata10810$characteristics_ch1.3
# Patterns to handle: "grade: 0", "grade: i", "grade: ii", "grade: iii"
pdata10810$grade <- standardize_grade(
grades10810,
c("grade: 0", "grade: i\\b", "grade: ii\\b", "grade: iii\\b"),
c("0", "1", "2", "3")
)
pdata10810 <- pdata10810[!is.na(pdata10810$grade), ]
### GSE17907
pdata17907 <- pData(gse17907)
grades17907 <- pdata17907$`grade sbr:ch1`
pdata17907$grade <- standardize_grade(
grades17907,
c("--"),
c("0")
)
pdata17907 <- pdata17907[!is.na(pdata17907$grade), ]
### GSE15852
pdata15852 <- pData(gse15852)
grades15852 <- pdata15852$`characteristics_ch1.2`
pdata15852$grade <- standardize_grade(
grades15852,
c("grade: normal", "grade: grade 1", "grade: grade 2", "grade: grade 3"),
c("0", "1", "2", "3")
)
pdata15852 <- pdata15852[!is.na(pdata15852$grade), ]Code
# visualising original mapping and then remapping
build_mapping_df <- function(original_labels, dataset_name, pattern, replacements) {
cleaned <- standardize_grade(original_labels, pattern, replacements, remove_na = FALSE)
data.frame(
Original = original_labels,
Cleaned = cleaned,
Dataset = dataset_name,
stringsAsFactors = FALSE
)
}
map_10810 <- build_mapping_df(
grades10810, "GSE10810",
c("grade: 0", "grade: i\\b", "grade: ii\\b", "grade: iii\\b"),
c("0", "1", "2", "3")
)
map_17907 <- build_mapping_df(
grades17907, "GSE17907",
c("--"),
c("0")
)
# Combine
all_maps <- bind_rows(map_10810, map_17907) %>%
filter(!is.na(Cleaned))
# Plot
og_dist_plot <- ggplot(all_maps, aes(x = Original, fill = Cleaned)) +
geom_bar() +
facet_wrap(~ Dataset, scales = "free_x") +
scale_fill_manual(values = c(
"Normal" = "#F8766D", "Grade1" = "#B79F00", "Grade2" = "#00BA38", "Grade3" = "#619CFF"
)) +
labs(title = "Mapping of Original Grade Labels to Standardized Names",
x = "Original Label", y = "Frequency", fill = "Standardized Grade") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
all_maps_long <- all_maps %>% count(Original, Cleaned) %>% filter(!is.na(Cleaned))
stand_vis <- ggplot(all_maps_long,
aes(axis1 = Original, axis2 = Cleaned, y = n)) +
geom_alluvium(aes(fill = Cleaned), width = 1/12) +
geom_stratum(width = 1/12, fill = "grey80", color = "black") +
geom_label(stat = "stratum", aes(label = after_stat(stratum))) +
scale_fill_manual(values = c(
"Normal" = "#F8766D", "Grade1" = "#B79F00", "Grade2" = "#00BA38", "Grade3" = "#619CFF"
)) +
labs(title = "Standardizing Grade Labels Across Datasets",
x = "Standardization Step", y = "Sample Count") +
theme_minimal()
stand_vis
Code
og_dist_plot
Code
#check if data contains all 4 classes
pdata_list <- list(
GSE10810 = pdata10810,
GSE17907 = pdata17907
)
required_grades <- c("Normal", "Grade1", "Grade2", "Grade3")
qualified_datasets <- names(Filter(function(pdata) {
all(required_grades %in% pdata$grade)
}, pdata_list))
cat("Datasets containing all four grades:", paste(qualified_datasets, collapse = ", "), "\n")Datasets containing all four grades: GSE10810, GSE17907 Code
# Merges datasets
# 1. Expression sets
expr_list <- list(
gse10810 = exprs(gse10810),
gse17907 = exprs(gse17907)
)
# 2. Phenotype sets
pdata_list <- list(
pdata10810,
pdata17907
)
# 3. Fix phenotype rownames using geo_accession if present
pdata_list <- lapply(pdata_list, function(p) {
if ("geo_accession" %in% colnames(p)) {
rownames(p) <- p$geo_accession
}
return(p)
})
# Reassign corrected pdata
# 1. Assign pdata
pdata10810 <- pdata_list[[1]]
pdata17907 <- pdata_list[[2]]
# 2. Create pheno_list with dataset names
pheno_list <- list(
gse10810 = pdata10810,
gse17907 = pdata17907
)
# 3. Extract grade list and track batch
grade_list <- Map(function(expr, pheno, batch_name) {
sample_names <- colnames(expr)
matched_pheno <- pheno[sample_names, , drop = FALSE]
grades <- matched_pheno$grade
names(grades) <- sample_names
# Track batch
data.frame(
sample = sample_names,
grade = grades,
batch = batch_name,
stringsAsFactors = FALSE
)
}, expr_list, pheno_list, names(pheno_list))
# 4. Combine all grade + batch info into metadata
metadata <- do.call(rbind, grade_list)
# 5. Clean sample names to remove prefixes (e.g., "gse10810.")
metadata$sample <- sub(".*\\.", "", metadata$sample)
# 6. Combine expression matrices
expr_dfs <- lapply(expr_list, function(mat) {
df <- as.data.frame(mat)
df$gene <- rownames(mat)
df
})
merged_expr <- Reduce(function(x, y) full_join(x, y, by = "gene"), expr_dfs)
rownames(merged_expr) <- merged_expr$gene
merged_expr$gene <- NULL
# 7. Ensure metadata matches expression matrix columns
colnames(merged_expr) <- sub(".*\\.", "", colnames(merged_expr)) # Match cleaned sample names
metadata <- metadata[match(colnames(merged_expr), metadata$sample), ]
# 8. Final check
stopifnot(all(metadata$sample == colnames(merged_expr)))
# ✅ Outputs
metadata sample grade batch
gse10810.GSM272923 GSM272923 Normal gse10810
gse10810.GSM272924 GSM272924 Normal gse10810
gse10810.GSM272925 GSM272925 Normal gse10810
gse10810.GSM272926 GSM272926 Normal gse10810
gse10810.GSM272927 GSM272927 Normal gse10810
gse10810.GSM272928 GSM272928 Normal gse10810
gse10810.GSM272929 GSM272929 Normal gse10810
gse10810.GSM272930 GSM272930 Normal gse10810
gse10810.GSM272931 GSM272931 Normal gse10810
gse10810.GSM272932 GSM272932 Normal gse10810
gse10810.GSM272933 GSM272933 Normal gse10810
gse10810.GSM272934 GSM272934 Normal gse10810
gse10810.GSM272935 GSM272935 Normal gse10810
gse10810.GSM272936 GSM272936 Normal gse10810
gse10810.GSM272937 GSM272937 Normal gse10810
gse10810.GSM272938 GSM272938 Normal gse10810
gse10810.GSM272939 GSM272939 Normal gse10810
gse10810.GSM272940 GSM272940 Normal gse10810
gse10810.GSM272941 GSM272941 Normal gse10810
gse10810.GSM272942 GSM272942 Normal gse10810
gse10810.GSM272943 GSM272943 Normal gse10810
gse10810.GSM272944 GSM272944 Normal gse10810
gse10810.GSM272945 GSM272945 Normal gse10810
gse10810.GSM272946 GSM272946 Normal gse10810
gse10810.GSM272947 GSM272947 Normal gse10810
gse10810.GSM272948 GSM272948 Normal gse10810
gse10810.GSM272949 GSM272949 Normal gse10810
gse10810.GSM272950 GSM272950 Grade3 gse10810
gse10810.GSM272951 GSM272951 Grade3 gse10810
gse10810.GSM272952 GSM272952 Grade2 gse10810
gse10810.GSM272953 GSM272953 <NA> gse10810
gse10810.GSM272954 GSM272954 <NA> gse10810
gse10810.GSM272955 GSM272955 Grade2 gse10810
gse10810.GSM272956 GSM272956 Grade1 gse10810
gse10810.GSM272957 GSM272957 <NA> gse10810
gse10810.GSM272958 GSM272958 Grade2 gse10810
gse10810.GSM272959 GSM272959 Grade3 gse10810
gse10810.GSM272960 GSM272960 Grade3 gse10810
gse10810.GSM272961 GSM272961 Grade3 gse10810
gse10810.GSM272962 GSM272962 Grade2 gse10810
gse10810.GSM272963 GSM272963 Grade3 gse10810
gse10810.GSM272964 GSM272964 <NA> gse10810
gse10810.GSM272965 GSM272965 Grade3 gse10810
gse10810.GSM272966 GSM272966 Grade2 gse10810
gse10810.GSM272967 GSM272967 <NA> gse10810
gse10810.GSM272968 GSM272968 <NA> gse10810
gse10810.GSM272969 GSM272969 Grade2 gse10810
gse10810.GSM272970 GSM272970 Grade2 gse10810
gse10810.GSM272971 GSM272971 Grade1 gse10810
gse10810.GSM272972 GSM272972 <NA> gse10810
gse10810.GSM272973 GSM272973 Grade3 gse10810
gse10810.GSM272974 GSM272974 Grade2 gse10810
gse10810.GSM272975 GSM272975 Grade2 gse10810
gse10810.GSM272976 GSM272976 <NA> gse10810
gse10810.GSM272977 GSM272977 Grade3 gse10810
gse10810.GSM272978 GSM272978 Grade3 gse10810
gse10810.GSM272979 GSM272979 <NA> gse10810
gse10810.GSM272980 GSM272980 Grade2 gse10810
gse17907.GSM447197 GSM447197 Grade3 gse17907
gse17907.GSM447198 GSM447198 Grade3 gse17907
gse17907.GSM447199 GSM447199 Grade3 gse17907
gse17907.GSM447200 GSM447200 Grade3 gse17907
gse17907.GSM447201 GSM447201 Grade3 gse17907
gse17907.GSM447202 GSM447202 Grade3 gse17907
gse17907.GSM447203 GSM447203 Grade3 gse17907
gse17907.GSM447204 GSM447204 Grade3 gse17907
gse17907.GSM447205 GSM447205 Grade3 gse17907
gse17907.GSM447206 GSM447206 Grade3 gse17907
gse17907.GSM447207 GSM447207 Grade2 gse17907
gse17907.GSM447208 GSM447208 Grade2 gse17907
gse17907.GSM447209 GSM447209 Grade3 gse17907
gse17907.GSM447210 GSM447210 Grade3 gse17907
gse17907.GSM447211 GSM447211 Grade3 gse17907
gse17907.GSM447212 GSM447212 Grade3 gse17907
gse17907.GSM447213 GSM447213 Grade2 gse17907
gse17907.GSM447214 GSM447214 Grade2 gse17907
gse17907.GSM447215 GSM447215 Grade3 gse17907
gse17907.GSM447216 GSM447216 Grade3 gse17907
gse17907.GSM447217 GSM447217 Grade2 gse17907
gse17907.GSM447218 GSM447218 Grade3 gse17907
gse17907.GSM447219 GSM447219 Grade3 gse17907
gse17907.GSM447220 GSM447220 Normal gse17907
gse17907.GSM447221 GSM447221 Grade3 gse17907
gse17907.GSM447222 GSM447222 Grade2 gse17907
gse17907.GSM447223 GSM447223 Grade3 gse17907
gse17907.GSM447224 GSM447224 Grade3 gse17907
gse17907.GSM447225 GSM447225 Grade3 gse17907
gse17907.GSM447226 GSM447226 Grade3 gse17907
gse17907.GSM447227 GSM447227 Grade2 gse17907
gse17907.GSM447228 GSM447228 Grade2 gse17907
gse17907.GSM447229 GSM447229 Grade3 gse17907
gse17907.GSM447230 GSM447230 Grade3 gse17907
gse17907.GSM447231 GSM447231 Grade3 gse17907
gse17907.GSM447232 GSM447232 Grade3 gse17907
gse17907.GSM447233 GSM447233 Grade3 gse17907
gse17907.GSM447234 GSM447234 Grade3 gse17907
gse17907.GSM447235 GSM447235 Normal gse17907
gse17907.GSM447236 GSM447236 Grade3 gse17907
gse17907.GSM447237 GSM447237 Normal gse17907
gse17907.GSM447238 GSM447238 Grade1 gse17907
gse17907.GSM447239 GSM447239 Grade3 gse17907
gse17907.GSM447240 GSM447240 Grade1 gse17907
gse17907.GSM447241 GSM447241 Normal gse17907
gse17907.GSM447242 GSM447242 Grade2 gse17907
gse17907.GSM447243 GSM447243 Grade2 gse17907
gse17907.GSM447244 GSM447244 Grade1 gse17907
gse17907.GSM447245 GSM447245 Grade3 gse17907
gse17907.GSM447246 GSM447246 Grade3 gse17907
gse17907.GSM447247 GSM447247 Grade3 gse17907
gse17907.GSM447248 GSM447248 Normal gse17907
gse17907.GSM447249 GSM447249 Normal gse17907
gse17907.GSM447250 GSM447250 Normal gse17907
gse17907.GSM447251 GSM447251 Normal gse17907Code
dim(merged_expr)[1] 26199 113Code
# visualisations of plots before/after NA removal
# 1. Original plot: Sample counts by batch and grade
plot_by_batch <- ggplot(metadata, aes(x = batch, fill = grade)) +
geom_bar(position = "dodge") +
labs(title = "Sample Counts by Dataset (Batch) and Grade",
x = "Dataset (Batch)", y = "Number of Samples", fill = "Grade") +
theme_minimal() +
scale_fill_manual(values = c(
"Normal" = "#F8766D", "Grade1" = "#B79F00", "Grade2" = "#00BA38", "Grade3" = "#619CFF"
))
# 2. Combined dataset grade distribution plot (ignoring batches)
plot_combined <- ggplot(metadata, aes(x = grade, fill = grade)) +
geom_bar() +
labs(title = "Combined Dataset Grade Distribution",
x = "Grade", y = "Number of Samples", fill = "Grade") +
theme_minimal() +
scale_fill_manual(values = c(
"Normal" = "#F8766D", "Grade1" = "#B79F00", "Grade2" = "#00BA38", "Grade3" = "#619CFF"
))
plot_by_batch
Code
plot_combined
Code
# Filtering by median expression
filter_low_expression <- function(expr_data, median_threshold = 5, sd_threshold = 0.5, filter_median = TRUE, filter_sd = TRUE) {
# If input is ExpressionSet, extract expression matrix
if (class(expr_data)[1] == "ExpressionSet") {
exprs_mat <- exprs(expr_data)
} else if (is.matrix(expr_data) || is.data.frame(expr_data)) {
exprs_mat <- as.matrix(expr_data)
} else {
stop("expr_data must be an ExpressionSet or a matrix/data.frame")
}
keep_median <- rep(TRUE, nrow(exprs_mat))
keep_sd <- rep(TRUE, nrow(exprs_mat))
if (filter_median) {
keep_median <- apply(exprs_mat, 1, median) > median_threshold
}
if (filter_sd) {
keep_sd <- apply(exprs_mat, 1, sd) > sd_threshold
}
keep <- keep_median & keep_sd
# Filter the expression matrix or ExpressionSet accordingly
if (class(expr_data)[1] == "ExpressionSet") {
filtered <- expr_data[keep, ]
} else {
filtered <- exprs_mat[keep, , drop = FALSE]
}
return(filtered)
}Code
# Visualisation of filteration process
# Compute statistics before filtering
gene_medians_before <- apply(merged_expr, 1, median)
gene_sds_before <- apply(merged_expr, 1, sd)
# Apply filter
filtered_expr <- filter_low_expression(merged_expr, median_threshold = 5, sd_threshold = 0.5)
# Compute statistics after filtering
gene_medians_after <- apply(filtered_expr, 1, median)
gene_sds_after <- apply(filtered_expr, 1, sd)
# Create data frame for ggplot
df_plot <- data.frame(
median = c(gene_medians_before, gene_medians_after),
sd = c(gene_sds_before, gene_sds_after),
status = rep(c("Before Filtering", "After Filtering"),
c(length(gene_medians_before), length(gene_medians_after)))
)
# Density plots
p1 <- ggplot(df_plot, aes(x = median, fill = status)) +
geom_density(alpha = 0.5) +
labs(title = "Gene Median Expression", x = "Median Expression", y = "Density") +
theme_minimal()
p2 <- ggplot(df_plot, aes(x = sd, fill = status)) +
geom_density(alpha = 0.5) +
labs(title = "Gene Expression SD", x = "Standard Deviation", y = "Density") +
theme_minimal()
# Display the plots
filter_plot <- p1 + p2
filter_plotWarning: Removed 18878 rows containing non-finite outside the scale range
(`stat_density()`).
Removed 18878 rows containing non-finite outside the scale range
(`stat_density()`).
Code
# filternig lowly expressed genes
merged_expr <- filter_low_expression(merged_expr, median_threshold = 5, sd_threshold = 0.5)
dim(merged_expr)[1] 23509 113Code
before_genes <- nrow(merged_expr) + sum(!complete.cases(merged_expr))
# Number of genes after filtering
after_genes <- nrow(merged_expr)
metadata <- metadata[!is.na(metadata$grade), ]
metadata sample grade batch
gse10810.GSM272923 GSM272923 Normal gse10810
gse10810.GSM272924 GSM272924 Normal gse10810
gse10810.GSM272925 GSM272925 Normal gse10810
gse10810.GSM272926 GSM272926 Normal gse10810
gse10810.GSM272927 GSM272927 Normal gse10810
gse10810.GSM272928 GSM272928 Normal gse10810
gse10810.GSM272929 GSM272929 Normal gse10810
gse10810.GSM272930 GSM272930 Normal gse10810
gse10810.GSM272931 GSM272931 Normal gse10810
gse10810.GSM272932 GSM272932 Normal gse10810
gse10810.GSM272933 GSM272933 Normal gse10810
gse10810.GSM272934 GSM272934 Normal gse10810
gse10810.GSM272935 GSM272935 Normal gse10810
gse10810.GSM272936 GSM272936 Normal gse10810
gse10810.GSM272937 GSM272937 Normal gse10810
gse10810.GSM272938 GSM272938 Normal gse10810
gse10810.GSM272939 GSM272939 Normal gse10810
gse10810.GSM272940 GSM272940 Normal gse10810
gse10810.GSM272941 GSM272941 Normal gse10810
gse10810.GSM272942 GSM272942 Normal gse10810
gse10810.GSM272943 GSM272943 Normal gse10810
gse10810.GSM272944 GSM272944 Normal gse10810
gse10810.GSM272945 GSM272945 Normal gse10810
gse10810.GSM272946 GSM272946 Normal gse10810
gse10810.GSM272947 GSM272947 Normal gse10810
gse10810.GSM272948 GSM272948 Normal gse10810
gse10810.GSM272949 GSM272949 Normal gse10810
gse10810.GSM272950 GSM272950 Grade3 gse10810
gse10810.GSM272951 GSM272951 Grade3 gse10810
gse10810.GSM272952 GSM272952 Grade2 gse10810
gse10810.GSM272955 GSM272955 Grade2 gse10810
gse10810.GSM272956 GSM272956 Grade1 gse10810
gse10810.GSM272958 GSM272958 Grade2 gse10810
gse10810.GSM272959 GSM272959 Grade3 gse10810
gse10810.GSM272960 GSM272960 Grade3 gse10810
gse10810.GSM272961 GSM272961 Grade3 gse10810
gse10810.GSM272962 GSM272962 Grade2 gse10810
gse10810.GSM272963 GSM272963 Grade3 gse10810
gse10810.GSM272965 GSM272965 Grade3 gse10810
gse10810.GSM272966 GSM272966 Grade2 gse10810
gse10810.GSM272969 GSM272969 Grade2 gse10810
gse10810.GSM272970 GSM272970 Grade2 gse10810
gse10810.GSM272971 GSM272971 Grade1 gse10810
gse10810.GSM272973 GSM272973 Grade3 gse10810
gse10810.GSM272974 GSM272974 Grade2 gse10810
gse10810.GSM272975 GSM272975 Grade2 gse10810
gse10810.GSM272977 GSM272977 Grade3 gse10810
gse10810.GSM272978 GSM272978 Grade3 gse10810
gse10810.GSM272980 GSM272980 Grade2 gse10810
gse17907.GSM447197 GSM447197 Grade3 gse17907
gse17907.GSM447198 GSM447198 Grade3 gse17907
gse17907.GSM447199 GSM447199 Grade3 gse17907
gse17907.GSM447200 GSM447200 Grade3 gse17907
gse17907.GSM447201 GSM447201 Grade3 gse17907
gse17907.GSM447202 GSM447202 Grade3 gse17907
gse17907.GSM447203 GSM447203 Grade3 gse17907
gse17907.GSM447204 GSM447204 Grade3 gse17907
gse17907.GSM447205 GSM447205 Grade3 gse17907
gse17907.GSM447206 GSM447206 Grade3 gse17907
gse17907.GSM447207 GSM447207 Grade2 gse17907
gse17907.GSM447208 GSM447208 Grade2 gse17907
gse17907.GSM447209 GSM447209 Grade3 gse17907
gse17907.GSM447210 GSM447210 Grade3 gse17907
gse17907.GSM447211 GSM447211 Grade3 gse17907
gse17907.GSM447212 GSM447212 Grade3 gse17907
gse17907.GSM447213 GSM447213 Grade2 gse17907
gse17907.GSM447214 GSM447214 Grade2 gse17907
gse17907.GSM447215 GSM447215 Grade3 gse17907
gse17907.GSM447216 GSM447216 Grade3 gse17907
gse17907.GSM447217 GSM447217 Grade2 gse17907
gse17907.GSM447218 GSM447218 Grade3 gse17907
gse17907.GSM447219 GSM447219 Grade3 gse17907
gse17907.GSM447220 GSM447220 Normal gse17907
gse17907.GSM447221 GSM447221 Grade3 gse17907
gse17907.GSM447222 GSM447222 Grade2 gse17907
gse17907.GSM447223 GSM447223 Grade3 gse17907
gse17907.GSM447224 GSM447224 Grade3 gse17907
gse17907.GSM447225 GSM447225 Grade3 gse17907
gse17907.GSM447226 GSM447226 Grade3 gse17907
gse17907.GSM447227 GSM447227 Grade2 gse17907
gse17907.GSM447228 GSM447228 Grade2 gse17907
gse17907.GSM447229 GSM447229 Grade3 gse17907
gse17907.GSM447230 GSM447230 Grade3 gse17907
gse17907.GSM447231 GSM447231 Grade3 gse17907
gse17907.GSM447232 GSM447232 Grade3 gse17907
gse17907.GSM447233 GSM447233 Grade3 gse17907
gse17907.GSM447234 GSM447234 Grade3 gse17907
gse17907.GSM447235 GSM447235 Normal gse17907
gse17907.GSM447236 GSM447236 Grade3 gse17907
gse17907.GSM447237 GSM447237 Normal gse17907
gse17907.GSM447238 GSM447238 Grade1 gse17907
gse17907.GSM447239 GSM447239 Grade3 gse17907
gse17907.GSM447240 GSM447240 Grade1 gse17907
gse17907.GSM447241 GSM447241 Normal gse17907
gse17907.GSM447242 GSM447242 Grade2 gse17907
gse17907.GSM447243 GSM447243 Grade2 gse17907
gse17907.GSM447244 GSM447244 Grade1 gse17907
gse17907.GSM447245 GSM447245 Grade3 gse17907
gse17907.GSM447246 GSM447246 Grade3 gse17907
gse17907.GSM447247 GSM447247 Grade3 gse17907
gse17907.GSM447248 GSM447248 Normal gse17907
gse17907.GSM447249 GSM447249 Normal gse17907
gse17907.GSM447250 GSM447250 Normal gse17907
gse17907.GSM447251 GSM447251 Normal gse17907Code
# 🧼 Remove corresponding columns from merged_expr
merged_expr <- merged_expr[, metadata$sample]
anyNA(merged_expr)[1] TRUECode
sum(is.na(merged_expr))[1] 981656Code
merged_expr <- merged_expr[complete.cases(merged_expr), ]
merged_expr GSM272923 GSM272924 GSM272925 GSM272926 GSM272927 GSM272928
1007_s_at 9.177956 7.954836 8.251062 8.614367 8.219889 8.762812
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117_at 6.474552 7.130595 6.836256 6.279254 6.291832 7.468263
1294_at 7.521690 7.531083 7.404266 7.435497 7.475999 7.550991
1405_i_at 6.299448 7.572516 6.903968 6.180391 6.543911 7.391294
1438_at 6.083649 5.615124 5.966308 6.009695 6.096937 5.086782
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1552264_a_at 7.004888 7.320888 7.470403 7.101328 7.082299 7.159826
1552274_at 6.872236 6.682208 7.502617 6.953521 7.185828 7.223533
1552275_s_at 7.267071 7.071657 7.366111 6.910617 7.159277 7.464649
1552277_a_at 6.869386 7.319260 7.033949 6.573118 6.665362 6.777419
1552287_s_at 6.652017 6.151219 5.814030 6.034020 5.952684 6.721537
1552309_a_at 6.632637 6.606272 7.300588 6.828253 7.095091 7.751047
1552310_at 6.982805 6.683536 6.418719 6.573219 6.823329 6.865242
1552312_a_at 6.117383 6.145299 6.364406 6.058368 6.389690 6.150782
1552315_at 6.861404 7.135674 7.111667 7.026240 7.133305 7.412301
1552316_a_at 7.762636 8.165492 7.756700 7.761472 7.723206 8.277100
1552318_at 5.731845 6.330002 6.726536 6.186099 6.055593 7.291802
1552347_at 8.479728 7.552028 7.708384 7.801216 8.298075 8.584863
1552426_a_at 8.852840 8.437425 8.848900 8.847612 8.228135 8.941636
1552474_a_at 7.443247 7.482115 7.431134 7.589120 7.444745 7.400499
1552477_a_at 6.295758 4.246914 4.381170 5.610911 5.198016 3.847529
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1552509_a_at 9.808004 9.777023 9.819077 9.890655 10.156965 10.497312
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1552575_a_at 6.059981 5.292493 6.012048 5.464325 6.233077 6.848540
1552610_a_at 9.407752 9.490459 9.148057 9.161976 9.284739 9.615733
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1552621_at 7.239846 6.034397 7.285726 6.577278 7.619390 7.628502
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[ reached getOption("max.print") -- omitted 13109 rows ]Code
table(metadata$grade)
Grade1 Grade2 Grade3 Normal
5 20 44 35 Code
# Number of genes before filtering
na_removal_grad_dist <- ggplot(metadata, aes(x = grade, fill = grade)) +
geom_bar() +
labs(title = "Combined Dataset Grade Distribution",
x = "Grade", y = "Number of Samples", fill = "Grade") +
theme_minimal() +
scale_fill_manual(values = c(
"Normal" = "#F8766D", "Grade1" = "#B79F00", "Grade2" = "#00BA38", "Grade3" = "#619CFF"
))
gene_counts <- data.frame(
Status = c("Before Removing NA Genes", "After Removing NA Genes"),
Count = c(before_genes, after_genes)
)
gene_na_removal <- ggplot(gene_counts, aes(x = Status, y = Count, fill = Status)) +
geom_bar(stat = "identity") +
labs(title = "Gene Counts Before and After Removing Genes with Missing Values",
y = "Number of Genes", x = "") +
theme_minimal() +
scale_fill_manual(values = c("Before Removing NA Genes" = "#619CFF", "After Removing NA Genes" = "#00BA38")) +
geom_text(aes(label = Count), vjust = -0.5)Code
library(sva)
# ========== 1. Input data ==========
# merged_expr_filtered: The merged expression matrix (row = gene, column = sample)
# metadata: A data frame containing sample, batch, and grade
# ========== 2. Prepare batch and grade information ==========
batch_all <- metadata$batch
grade_all <- factor(metadata$grade)
mod_all <- model.matrix(~ grade_all)
# ========== 3. Remove genes with variance of 0 ==========
remove_zero_var_genes <- function(expr, batch) {
keep <- apply(expr, 1, function(gene) {
all(tapply(gene, batch, function(x) var(x) > 0))
})
expr[keep, ]
}
expr_clean <- remove_zero_var_genes(merged_expr, batch_all)
# ========== 4. Batch effect correction ==========
expr_corrected <- ComBat(dat = expr_clean, batch = batch_all, mod = mod_all)Found2batchesAdjusting for3covariate(s) or covariate level(s)Standardizing Data across genesFitting L/S model and finding priorsFinding parametric adjustmentsAdjusting the DataCode
library(ggplot2)
library(ggfortify)
library(RColorBrewer)
library(patchwork)
# Define a pastel pink color palette (or create your own)
pastel_pinks <- c("#FBB1BD", "#FDC8D1", "#FDDDE5", "#FFE5ED", "#F8AFA6", "#E8C7CD")
# Perform PCA
pca_before <- prcomp(t(expr_clean), scale. = TRUE)
pca_after <- prcomp(t(expr_corrected), scale. = TRUE)
# Extract PCA data
pca_df_before <- data.frame(pca_before$x[, 1:2], metadata)
pca_df_after <- data.frame(pca_after$x[, 1:2], metadata)
# Define a custom pink/purple palette
batch_levels <- unique(metadata$batch)
color_palette <- c("#E8C7Cd", "#FBB1BD", "#E786D7", "#B07AA1", "#D36EAF", "#C77CFF")
colors <- setNames(color_palette[seq_along(batch_levels)], batch_levels)
# Base plot function
plot_pca <- function(df, title) {
ggplot(df, aes(x = PC1, y = PC2, color = batch)) +
geom_point(size = 1.5) +
stat_ellipse(type = "norm", geom = "path", linewidth = 1) + # Only border, no fill
scale_color_manual(values = colors) +
labs(title = title, color = "Batch") +
theme_minimal() +
theme(legend.position = "bottom")
}
# Generate plots
p1 <- plot_pca(pca_df_before, "Before Batch Correction")
p2 <- plot_pca(pca_df_after, "After Batch Correction")
# Display side by side
bat_pca <- p1 + p2
bat_pca
Code
RNGkind(kind = "L'Ecuyer-CMRG")
set.seed(123)
# ========== Train/Test Split ==========
split_by_grade <- split(metadata, metadata$grade)
train_samples <- unlist(lapply(split_by_grade, function(df) {
n <- nrow(df)
if (n >= 2) {
sample(df$sample, size = floor(0.8 * n))
} else {
sample(df$sample, size = 1)
}
}))
test_samples <- setdiff(metadata$sample, train_samples)
expr_train <- expr_corrected[, train_samples]
expr_test <- expr_corrected[, test_samples]
metadata_train <- metadata[match(train_samples, metadata$sample), ]
metadata_test <- metadata[match(test_samples, metadata$sample), ]
metadata_train$grade <- factor(metadata_train$grade,
levels = c("Normal", "Grade1", "Grade2", "Grade3"))
metadata_test$grade <- factor(metadata_test$grade,
levels = c("Normal", "Grade1", "Grade2", "Grade3"))
levels(metadata_train$grade) <- make.names(levels(metadata_train$grade))
levels(metadata_test$grade) <- make.names(levels(metadata_test$grade))
cat("Training set grade counts:\n")Training set grade counts:Code
print(table(metadata_train$grade))
Normal Grade1 Grade2 Grade3
28 4 16 35 Code
cat("Test set grade counts:\n")Test set grade counts:Code
print(table(metadata_test$grade))
Normal Grade1 Grade2 Grade3
7 1 4 9 Code
library(smotefamily)
# Transpose so samples are rows
expr_t <- t(expr_train)
expr_df <- as.data.frame(expr_t)
expr_df$grade <- metadata_train$grade[match(rownames(expr_df), metadata_train$sample)]
# Convert grade to factor
expr_df$grade <- as.factor(expr_df$grade)
# Apply SMOTE (over-sampling only)
# dup_size controls how many synthetic samples to make per minority sample
smote_result <- SMOTE(X = expr_df[, -ncol(expr_df)],
target = expr_df$grade,
K = 3,
dup_size = 5) # try 5–10
# SMOTE returns a list with $data
expr_smote <- smote_result$data
# Extract new metadata and expression matrix
metadata_train <- data.frame(
sample = paste0("SMOTE_", seq_len(nrow(expr_smote))),
grade = expr_smote$class
)
expr_balanced <- t(as.matrix(expr_smote[, -ncol(expr_smote)]))
colnames(expr_balanced) <- metadata_train$sample
metadata_train$grade <- factor(
expr_smote$class,
levels = c("Normal", "Grade1", "Grade2", "Grade3")
)Code
library(ggplot2)
library(ggplot2)
library(uwot)Loading required package: MatrixCode
grade_levels <- c("Normal", "Grade1", "Grade2", "Grade3")
grade_palette <- c(
"Normal" = "#F8766D", # bright coral pink
"Grade1" = "#B79F00", # gold-brown
"Grade2" = "#00BA38", # vivid green
"Grade3" = "#619CFF" # sky blue
)
before <- data.frame(grade = expr_df$grade, source = "Original")
after <- data.frame(grade = expr_smote$class, source = "SMOTE")
combined <- rbind(before, after)
# Ensure factor levels for consistent color ordering
combined$grade <- factor(combined$grade, levels = grade_levels)
smote_bar <- ggplot(combined, aes(x = grade, fill = source)) +
geom_bar(position = "dodge") +
scale_fill_manual(values = c("Original" = "#E8C7Cd", "SMOTE" = "#FBB1BD")) +
scale_x_discrete(drop = FALSE) +
labs(title = "Class Distribution Before and After SMOTE",
x = "Grade", y = "Count") +
theme_minimal()
# Combine original and synthetic
expr_combined <- rbind(expr_df[, -ncol(expr_df)], expr_smote[, -ncol(expr_smote)])
labels <- c(rep("Original", nrow(expr_df)), rep("SMOTE", nrow(expr_smote)))
grades <- c(as.character(expr_df$grade), as.character(expr_smote$class))
# PCA
pca <- prcomp(expr_combined, scale. = TRUE)
pca_df <- data.frame(pca$x[, 1:2], Source = labels, Grade = grades)
# Ensure consistent factor levels
pca_df$Grade <- factor(pca_df$Grade, levels = grade_levels)
smote_scatter <- ggplot(pca_df, aes(x = PC1, y = PC2, color = Grade, shape = Source)) +
geom_point(alpha = 0.7) +
scale_color_manual(values = grade_palette) +
labs(title = "PCA: Real vs SMOTE Samples", color = "Grade") +
theme_minimal()
smote_bar
Code
smote_scatter
Code
# Feature Selection on SMOTE-balanced Data
labels_factor <- factor(metadata_train$grade, levels = c("Normal", "Grade1", "Grade2", "Grade3"))
design <- model.matrix(~ 0 + labels_factor)
colnames(design) <- levels(labels_factor)
fit <- lmFit(expr_balanced, design)
fit <- eBayes(fit)
res <- topTable(fit, number = Inf, adjust.method = "BH")
# Significant genes (adjusted p < 0.05)
sig_genes <- rownames(subset(res, adj.P.Val < 0.05))
# Group means and strong difference filtering (max - min > 1)
group_means <- sapply(levels(labels_factor), function(lv) {
rowMeans(expr_balanced[, metadata_train$grade == lv, drop = FALSE])
})
expr_diff <- apply(group_means, 1, function(x) max(x) - min(x))
sig_genes_strong <- sig_genes[expr_diff[sig_genes] > 1]
# Sort by adjusted p-value
res_sig_strong <- res[sig_genes_strong, ]
res_sig_strong <- res_sig_strong[order(res_sig_strong$adj.P.Val), ]
# Build top gene sets only if enough genes exist
top_gene_sets <- list()
if (nrow(res_sig_strong) >= 30) top_gene_sets$top30 <- rownames(res_sig_strong)[1:30]
if (nrow(res_sig_strong) >= 50) top_gene_sets$top50 <- rownames(res_sig_strong)[1:50]
if (nrow(res_sig_strong) >= 80) top_gene_sets$top80 <- rownames(res_sig_strong)[1:80]
if (nrow(res_sig_strong) >= 110) top_gene_sets$top110 <- rownames(res_sig_strong)[1:110]
# Optional: show available gene set sizes
cat("Available gene sets:\n")Available gene sets:Code
print(names(top_gene_sets))[1] "top30" "top50" "top80" "top110"Code
# Load libraries
library(ComplexHeatmap)Loading required package: grid========================================
ComplexHeatmap version 2.22.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
If you use it in published research, please cite either one:
- Gu, Z. Complex Heatmap Visualization. iMeta 2022.
- Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
genomic data. Bioinformatics 2016.
The new InteractiveComplexHeatmap package can directly export static
complex heatmaps into an interactive Shiny app with zero effort. Have a try!
This message can be suppressed by:
suppressPackageStartupMessages(library(ComplexHeatmap))
========================================
Attaching package: 'ComplexHeatmap'The following object is masked from 'package:genefilter':
dist2The following object is masked from 'package:R.utils':
drawCode
library(circlize)========================================
circlize version 0.4.16
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/
If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
in R. Bioinformatics 2014.
This message can be suppressed by:
suppressPackageStartupMessages(library(circlize))
========================================Code
library(hgu133plus2.db) # For mapping probe IDs to gene symbolsLoading required package: AnnotationDbiLoading required package: stats4Loading required package: IRangesLoading required package: S4Vectors
Attaching package: 'S4Vectors'The following objects are masked from 'package:Matrix':
expand, unnameThe following objects are masked from 'package:dplyr':
first, renameThe following object is masked from 'package:utils':
findMatchesThe following objects are masked from 'package:base':
expand.grid, I, unname
Attaching package: 'IRanges'The following object is masked from 'package:nlme':
collapseThe following objects are masked from 'package:dplyr':
collapse, desc, sliceThe following object is masked from 'package:R.oo':
trim
Attaching package: 'AnnotationDbi'The following object is masked from 'package:dplyr':
selectLoading required package: org.Hs.eg.dbCode
library(AnnotationDbi)
# Subset expression data and scale by gene (Z-score)
selected_genes <- top_gene_sets$top30
expr_selected <- expr_balanced[selected_genes, ]
expr_selected <- t(scale(t(expr_selected))) # row-wise Z-score
# Grade annotation: different shades of purple
grade_colors <- c(
"Normal" = "#d8b5d8",
"Grade1" = "#b279b2",
"Grade2" = "#944f94",
"Grade3" = "#6a2e6a"
)
grade_annotation <- HeatmapAnnotation(
Grade = metadata_train$grade,
col = list(Grade = grade_colors),
annotation_name_side = "left"
)
# Row order by significance
gene_order <- rownames(res_sig_strong)[rownames(res_sig_strong) %in% selected_genes]
expr_selected <- expr_selected[gene_order, ]
# Convert probe IDs to gene symbols
gene_map <- AnnotationDbi::select(hgu133plus2.db,
keys = rownames(expr_selected),
columns = c("SYMBOL"),
keytype = "PROBEID")'select()' returned 1:1 mapping between keys and columnsCode
# Remove duplicates and map
gene_map <- gene_map[!duplicated(gene_map$PROBEID), ]
gene_symbols <- gene_map$SYMBOL[match(rownames(expr_selected), gene_map$PROBEID)]
# Replace rownames (probes) with gene symbols
rownames(expr_selected) <- ifelse(is.na(gene_symbols), rownames(expr_selected), gene_symbols)
# Define pink color gradient for Z-scores
z_col_fun <- colorRamp2(c(-2, 0, 2), c("#fbeffb", "#f7b6d2", "#c51b8a"))
# Create heatmap
gene_heat <- Heatmap(
expr_selected,
name = "Z-score",
col = z_col_fun,
bottom_annotation = grade_annotation,
cluster_columns = TRUE,
cluster_rows = TRUE, # Cluster genes
show_row_names = TRUE,
show_column_names = FALSE,
row_names_gp = gpar(fontsize = 10),
column_title = "Samples",
row_title = "Top Genes (adj.P.Val)",
heatmap_legend_param = list(title = "Z-score")
)
gene_heat
Code
library(caret)
library(ggplot2)
library(tidyr)
Attaching package: 'tidyr'The following object is masked from 'package:S4Vectors':
expandThe following objects are masked from 'package:Matrix':
expand, pack, unpackThe following object is masked from 'package:reshape2':
smithsThe following object is masked from 'package:R.utils':
extractCode
expr_train <- expr_balanced
set.seed(123)
results <- list()
cv_folds <- 5
# ----------------- Create reproducible seeds -----------------
generate_seed_list <- function(num_resamples, max_tune_length) {
set.seed(123)
seed_list <- vector(mode = "list", length = num_resamples + 1)
for (i in 1:num_resamples) {
seed_list[[i]] <- sample.int(1e6, max_tune_length)
}
seed_list[[num_resamples + 1]] <- sample.int(1e6, 1)
return(seed_list)
}
svm_grid <- data.frame(C = 1)
knn_grid <- expand.grid(k = 5:10)
rf_grid <- expand.grid(mtry = floor(sqrt(ncol(expr_train))))
max_tune_length <- max(nrow(svm_grid), nrow(knn_grid), nrow(rf_grid))
seeds <- generate_seed_list(cv_folds, max_tune_length)
# ----------------- Begin training loop -----------------
y_train <- as.factor(metadata_train$grade)
levels(y_train) <- make.names(levels(y_train))
y_test <- make.names(metadata_test$grade)
acc_list <- list()
summary_list <- list()
for (gene_set_name in names(top_gene_sets)) {
gene_set <- top_gene_sets[[gene_set_name]]
x_train <- t(expr_train[gene_set, , drop = FALSE])
x_test <- t(expr_test[gene_set, , drop = FALSE])
trctrl <- trainControl(
method = "cv",
number = cv_folds,
classProbs = TRUE,
savePredictions = "final",
seeds = seeds
)
# Model training
svm_mod <- train(x = x_train, y = y_train, method = "svmLinear", trControl = trctrl, tuneGrid = svm_grid)
knn_mod <- train(x = x_train, y = y_train, method = "knn", trControl = trctrl, tuneGrid = knn_grid)
rf_mod <- train(x = x_train, y = y_train, method = "rf", trControl = trctrl, tuneGrid = rf_grid, ntree = 100)
# ---------- Save CV results ----------
acc_list[[length(acc_list) + 1]] <- data.frame(GeneSet = gene_set_name, Model = "SVM", Accuracy = svm_mod$resample$Accuracy)
acc_list[[length(acc_list) + 1]] <- data.frame(GeneSet = gene_set_name, Model = "KNN", Accuracy = knn_mod$resample$Accuracy)
acc_list[[length(acc_list) + 1]] <- data.frame(GeneSet = gene_set_name, Model = "RF", Accuracy = rf_mod$resample$Accuracy)
# ---------- Test set evaluation ----------
test_preds <- list(
SVM = predict(svm_mod, x_test),
KNN = predict(knn_mod, x_test),
RF = predict(rf_mod, x_test)
)
for (model_name in names(test_preds)) {
pred <- factor(test_preds[[model_name]], levels = unique(y_test))
truth <- factor(y_test, levels = unique(y_test))
cm <- confusionMatrix(pred, truth)
acc <- cm$overall["Accuracy"]
kappa <- cm$overall["Kappa"]
macro_f1 <- mean(cm$byClass[, "F1"], na.rm = TRUE)
macro_precision <- mean(cm$byClass[, "Precision"], na.rm = TRUE)
macro_recall <- mean(cm$byClass[, "Recall"], na.rm = TRUE)
bal_acc <- mean(cm$byClass[, "Balanced Accuracy"], na.rm = TRUE)
macro_sens <- mean(cm$byClass[, "Sensitivity"], na.rm = TRUE)
macro_spec <- mean(cm$byClass[, "Specificity"], na.rm = TRUE)
summary_list[[length(summary_list) + 1]] <- data.frame(
GeneSet = gene_set_name,
Model = model_name,
Accuracy = round(acc, 4),
MacroF1 = round(macro_f1, 4),
MacroPrecision = round(macro_precision, 4),
MacroRecall = round(macro_recall, 4),
BalancedAccuracy = round(bal_acc, 4),
MacroSensitivity = round(macro_sens, 4),
MacroSpecificity = round(macro_spec, 4),
Kappa = round(kappa, 4)
)
cat("\n======", gene_set_name, "-", model_name, "======\n")
print(cm$table)
cat("Test Accuracy:", round(acc, 3),
"| Kappa:", round(kappa, 3),
"| Macro F1:", round(macro_f1, 3),
"| Precision:", round(macro_precision, 3),
"| Recall:", round(macro_recall, 3),
"| Sensitivity:", round(macro_sens, 3),
"| Specificity:", round(macro_spec, 3),
"| Balanced Accuracy:", round(bal_acc, 3), "\n")
}
}
====== top30 - SVM ======
Reference
Prediction Normal Grade1 Grade2 Grade3
Normal 6 0 1 1
Grade1 0 0 1 1
Grade2 0 0 0 0
Grade3 1 1 2 7
Test Accuracy: 0.619 | Kappa: 0.408 | Macro F1: 0.75 | Precision: 0.462 | Recall: 0.409 | Sensitivity: 0.409 | Specificity: 0.856 | Balanced Accuracy: 0.632
====== top30 - KNN ======
Reference
Prediction Normal Grade1 Grade2 Grade3
Normal 6 0 0 2
Grade1 0 0 1 2
Grade2 0 0 0 0
Grade3 1 1 3 5
Test Accuracy: 0.524 | Kappa: 0.281 | Macro F1: 0.663 | Precision: 0.417 | Recall: 0.353 | Sensitivity: 0.353 | Specificity: 0.823 | Balanced Accuracy: 0.588
====== top30 - RF ======
Reference
Prediction Normal Grade1 Grade2 Grade3
Normal 6 1 0 2
Grade1 0 0 1 1
Grade2 1 0 1 1
Grade3 0 0 2 5
Test Accuracy: 0.571 | Kappa: 0.372 | Macro F1: 0.554 | Precision: 0.429 | Recall: 0.416 | Sensitivity: 0.416 | Specificity: 0.85 | Balanced Accuracy: 0.633
====== top50 - SVM ======
Reference
Prediction Normal Grade1 Grade2 Grade3
Normal 5 0 0 1
Grade1 0 0 1 1
Grade2 0 0 0 0
Grade3 2 1 3 7
Test Accuracy: 0.571 | Kappa: 0.325 | Macro F1: 0.703 | Precision: 0.457 | Recall: 0.373 | Sensitivity: 0.373 | Specificity: 0.832 | Balanced Accuracy: 0.603
====== top50 - KNN ======
Reference
Prediction Normal Grade1 Grade2 Grade3
Normal 6 0 0 1
Grade1 0 0 2 3
Grade2 0 0 0 0
Grade3 1 1 2 5
Test Accuracy: 0.524 | Kappa: 0.314 | Macro F1: 0.706 | Precision: 0.471 | Recall: 0.353 | Sensitivity: 0.353 | Specificity: 0.836 | Balanced Accuracy: 0.595
====== top50 - RF ======
Reference
Prediction Normal Grade1 Grade2 Grade3
Normal 6 0 0 1
Grade1 0 0 1 1
Grade2 0 0 2 2
Grade3 1 1 1 5
Test Accuracy: 0.619 | Kappa: 0.444 | Macro F1: 0.648 | Precision: 0.496 | Recall: 0.478 | Sensitivity: 0.478 | Specificity: 0.865 | Balanced Accuracy: 0.672
====== top80 - SVM ======
Reference
Prediction Normal Grade1 Grade2 Grade3
Normal 5 0 0 0
Grade1 0 0 0 1
Grade2 0 0 0 0
Grade3 2 1 4 8
Test Accuracy: 0.619 | Kappa: 0.378 | Macro F1: 0.75 | Precision: 0.511 | Recall: 0.401 | Sensitivity: 0.401 | Specificity: 0.842 | Balanced Accuracy: 0.621
====== top80 - KNN ======
Reference
Prediction Normal Grade1 Grade2 Grade3
Normal 6 0 0 0
Grade1 0 0 1 2
Grade2 0 1 1 0
Grade3 1 0 2 7
Test Accuracy: 0.667 | Kappa: 0.507 | Macro F1: 0.664 | Precision: 0.55 | Recall: 0.471 | Sensitivity: 0.471 | Specificity: 0.885 | Balanced Accuracy: 0.678
====== top80 - RF ======
Reference
Prediction Normal Grade1 Grade2 Grade3
Normal 6 0 0 1
Grade1 0 0 0 1
Grade2 0 0 3 1
Grade3 1 1 1 6
Test Accuracy: 0.714 | Kappa: 0.571 | Macro F1: 0.758 | Precision: 0.568 | Recall: 0.568 | Sensitivity: 0.568 | Specificity: 0.892 | Balanced Accuracy: 0.73
====== top110 - SVM ======
Reference
Prediction Normal Grade1 Grade2 Grade3
Normal 5 0 0 0
Grade1 0 0 1 1
Grade2 0 0 1 0
Grade3 2 1 2 8
Test Accuracy: 0.667 | Kappa: 0.481 | Macro F1: 0.654 | Precision: 0.654 | Recall: 0.463 | Sensitivity: 0.463 | Specificity: 0.871 | Balanced Accuracy: 0.667
====== top110 - KNN ======
Reference
Prediction Normal Grade1 Grade2 Grade3
Normal 6 0 0 1
Grade1 1 0 2 4
Grade2 0 1 1 1
Grade3 0 0 1 3
Test Accuracy: 0.476 | Kappa: 0.315 | Macro F1: 0.535 | Precision: 0.485 | Recall: 0.36 | Sensitivity: 0.36 | Specificity: 0.844 | Balanced Accuracy: 0.602
====== top110 - RF ======
Reference
Prediction Normal Grade1 Grade2 Grade3
Normal 6 1 0 2
Grade1 0 0 1 1
Grade2 0 0 2 2
Grade3 1 0 1 4
Test Accuracy: 0.571 | Kappa: 0.382 | Macro F1: 0.594 | Precision: 0.458 | Recall: 0.45 | Sensitivity: 0.45 | Specificity: 0.85 | Balanced Accuracy: 0.65 Code
acc_df <- do.call(rbind, acc_list)
summary_df <- do.call(rbind, summary_list)
desired_order <- c("top30", "top50", "top80", "top110")
acc_df$GeneSet <- factor(acc_df$GeneSet, levels = desired_order)
summary_df$GeneSet <- factor(summary_df$GeneSet, levels = desired_order)
# -------- Plot the CV accuracy boxplot --------
combine_cv_plot <- ggplot(acc_df, aes(x = GeneSet, y = Accuracy, fill = Model)) +
geom_boxplot(position = position_dodge(0.8), outlier.shape = NA) +
geom_jitter(position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8), size = 1, alpha = 0.6) +
labs(
title = "Model Accuracy (Linear selection)",
x = "Top Gene Set",
y = "Cross-validated Accuracy"
) +
theme_minimal(base_size = 13) +
theme(legend.position = "top")
# -------- Plot the Test evaluation metric bar chart --------
eval_long_combined <- pivot_longer(summary_df,
cols = c("Accuracy", "MacroF1", "MacroPrecision", "MacroRecall", "MacroSensitivity", "MacroSpecificity", "BalancedAccuracy", "Kappa"),
names_to = "Metric", values_to = "Value")
combine_eval_plot <- ggplot(eval_long_combined , aes(x = GeneSet, y = Value, fill = Model)) +
geom_col(position = position_dodge(0.8)) +
facet_wrap(~ Metric, scales = "free_y") +
labs(
title = "Test Set Evaluation: Extended Metrics",
x = "Top Gene Set",
y = "Metric"
) +
theme_minimal(base_size = 13) +
theme(legend.position = "top")
combine_cv_plot
Code
combine_eval_plot
Code
dim(y_train)NULLCode
dim(y_test)NULL8.1.1.1 dataframe of result for combined
Code
combine_metrics <- tidyr::pivot_wider(
eval_long_combined,
names_from = Metric,
values_from = Value
)
combine_metrics# A tibble: 12 × 10
GeneSet Model Accuracy MacroF1 MacroPrecision MacroRecall MacroSensitivity
<fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 top30 SVM 0.619 0.75 0.462 0.409 0.409
2 top30 KNN 0.524 0.663 0.417 0.353 0.353
3 top30 RF 0.571 0.554 0.429 0.416 0.416
4 top50 SVM 0.571 0.703 0.457 0.373 0.373
5 top50 KNN 0.524 0.706 0.471 0.353 0.353
6 top50 RF 0.619 0.648 0.496 0.478 0.478
7 top80 SVM 0.619 0.75 0.511 0.401 0.401
8 top80 KNN 0.667 0.664 0.55 0.471 0.471
9 top80 RF 0.714 0.758 0.568 0.568 0.568
10 top110 SVM 0.667 0.654 0.654 0.463 0.463
11 top110 KNN 0.476 0.535 0.485 0.360 0.360
12 top110 RF 0.571 0.594 0.458 0.450 0.450
# ℹ 3 more variables: MacroSpecificity <dbl>, BalancedAccuracy <dbl>,
# Kappa <dbl>Code
# Add an identifier for easier plotting
combine_metrics$Config <- paste(combine_metrics$Model, combine_metrics$GeneSet, sep = "_")
# Select top 5 models by Balanced Accuracy
top5 <- combine_metrics %>%
arrange(desc(BalancedAccuracy)) %>%
head(5)
# Reshape to long format for ggplot
top5_long <- melt(top5, id.vars = c("Model", "GeneSet", "Config"))
# Plot heatmap
combine_heat <- ggplot(top5_long, aes(x = variable, y = Config, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient(low = "white", high = "#d07095", name = "Metric Value") +
labs(title = "Performance Heatmap of Top 5 Models",
x = "Metric",
y = "Model (Config)") +
theme_minimal(base_size = 14) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
combine_heat
Code
#Put the first five into the list
pdata_list <- list(
GSE10810 = pdata10810,
GSE17907 = pdata17907
)
required_grades <- c("Normal", "Grade1", "Grade2", "Grade3")
qualified_datasets <- names(Filter(function(pdata) {
all(required_grades %in% pdata$grade)
}, pdata_list))
cat("Datasets containing all four grades:", paste(qualified_datasets, collapse = ", "), "\n")Datasets containing all four grades: GSE10810, GSE17907 8.1.2 gse17907 code
Code
# 1. Expression sets
expr_list <- list(
gse17907 = exprs(gse17907)
)
# 2. Phenotype sets
pdata_list <- list(
pdata17907
)
# 3. Fix phenotype rownames using geo_accession if present
pdata_list <- lapply(pdata_list, function(p) {
if ("geo_accession" %in% colnames(p)) {
rownames(p) <- p$geo_accession
}
return(p)
})
# Reassign corrected pdata
# 1. Assign pdata
pdata17907 <- pdata_list[[1]]
# 2. Create pheno_list with dataset names
pheno_list <- list(
gse17907 = pdata17907
)
# 3. Extract grade list and track batch
grade_list <- Map(function(expr, pheno, batch_name) {
sample_names <- colnames(expr)
matched_pheno <- pheno[sample_names, , drop = FALSE]
grades <- matched_pheno$grade
names(grades) <- sample_names
# Track batch
data.frame(
sample = sample_names,
grade = grades,
batch = batch_name,
stringsAsFactors = FALSE
)
}, expr_list, pheno_list, names(pheno_list))
# 4. Combine all grade + batch info into metadata
metadata <- do.call(rbind, grade_list)
# 5. Clean sample names to remove prefixes (e.g., "gse10810.")
metadata$sample <- sub(".*\\.", "", metadata$sample)
# 6. Combine expression matrices
expr_dfs <- lapply(expr_list, function(mat) {
df <- as.data.frame(mat)
df$gene <- rownames(mat)
df
})
merged_expr <- Reduce(function(x, y) full_join(x, y, by = "gene"), expr_dfs)
rownames(merged_expr) <- merged_expr$gene
merged_expr$gene <- NULL
# 7. Ensure metadata matches expression matrix columns
colnames(merged_expr) <- sub(".*\\.", "", colnames(merged_expr)) # Match cleaned sample names
metadata <- metadata[match(colnames(merged_expr), metadata$sample), ]
# 8. Final check
stopifnot(all(metadata$sample == colnames(merged_expr)))
# ✅ Outputs
metadata sample grade batch
gse17907.GSM447197 GSM447197 Grade3 gse17907
gse17907.GSM447198 GSM447198 Grade3 gse17907
gse17907.GSM447199 GSM447199 Grade3 gse17907
gse17907.GSM447200 GSM447200 Grade3 gse17907
gse17907.GSM447201 GSM447201 Grade3 gse17907
gse17907.GSM447202 GSM447202 Grade3 gse17907
gse17907.GSM447203 GSM447203 Grade3 gse17907
gse17907.GSM447204 GSM447204 Grade3 gse17907
gse17907.GSM447205 GSM447205 Grade3 gse17907
gse17907.GSM447206 GSM447206 Grade3 gse17907
gse17907.GSM447207 GSM447207 Grade2 gse17907
gse17907.GSM447208 GSM447208 Grade2 gse17907
gse17907.GSM447209 GSM447209 Grade3 gse17907
gse17907.GSM447210 GSM447210 Grade3 gse17907
gse17907.GSM447211 GSM447211 Grade3 gse17907
gse17907.GSM447212 GSM447212 Grade3 gse17907
gse17907.GSM447213 GSM447213 Grade2 gse17907
gse17907.GSM447214 GSM447214 Grade2 gse17907
gse17907.GSM447215 GSM447215 Grade3 gse17907
gse17907.GSM447216 GSM447216 Grade3 gse17907
gse17907.GSM447217 GSM447217 Grade2 gse17907
gse17907.GSM447218 GSM447218 Grade3 gse17907
gse17907.GSM447219 GSM447219 Grade3 gse17907
gse17907.GSM447220 GSM447220 Normal gse17907
gse17907.GSM447221 GSM447221 Grade3 gse17907
gse17907.GSM447222 GSM447222 Grade2 gse17907
gse17907.GSM447223 GSM447223 Grade3 gse17907
gse17907.GSM447224 GSM447224 Grade3 gse17907
gse17907.GSM447225 GSM447225 Grade3 gse17907
gse17907.GSM447226 GSM447226 Grade3 gse17907
gse17907.GSM447227 GSM447227 Grade2 gse17907
gse17907.GSM447228 GSM447228 Grade2 gse17907
gse17907.GSM447229 GSM447229 Grade3 gse17907
gse17907.GSM447230 GSM447230 Grade3 gse17907
gse17907.GSM447231 GSM447231 Grade3 gse17907
gse17907.GSM447232 GSM447232 Grade3 gse17907
gse17907.GSM447233 GSM447233 Grade3 gse17907
gse17907.GSM447234 GSM447234 Grade3 gse17907
gse17907.GSM447235 GSM447235 Normal gse17907
gse17907.GSM447236 GSM447236 Grade3 gse17907
gse17907.GSM447237 GSM447237 Normal gse17907
gse17907.GSM447238 GSM447238 Grade1 gse17907
gse17907.GSM447239 GSM447239 Grade3 gse17907
gse17907.GSM447240 GSM447240 Grade1 gse17907
gse17907.GSM447241 GSM447241 Normal gse17907
gse17907.GSM447242 GSM447242 Grade2 gse17907
gse17907.GSM447243 GSM447243 Grade2 gse17907
gse17907.GSM447244 GSM447244 Grade1 gse17907
gse17907.GSM447245 GSM447245 Grade3 gse17907
gse17907.GSM447246 GSM447246 Grade3 gse17907
gse17907.GSM447247 GSM447247 Grade3 gse17907
gse17907.GSM447248 GSM447248 Normal gse17907
gse17907.GSM447249 GSM447249 Normal gse17907
gse17907.GSM447250 GSM447250 Normal gse17907
gse17907.GSM447251 GSM447251 Normal gse17907Code
filter_low_expression <- function(expr_data, median_threshold = 5, sd_threshold = 0.5, filter_median = TRUE, filter_sd = TRUE) {
# expr_data: expression matrix (genes x samples) or ExpressionSet object
# median_threshold: genes with median expression below this will be filtered out
# sd_threshold: genes with SD below this will be filtered out
# filter_median: whether to filter by median expression
# filter_sd: whether to filter by standard deviation (variance)
# If input is ExpressionSet, extract expression matrix
if (class(expr_data)[1] == "ExpressionSet") {
exprs_mat <- exprs(expr_data)
} else if (is.matrix(expr_data) || is.data.frame(expr_data)) {
exprs_mat <- as.matrix(expr_data)
} else {
stop("expr_data must be an ExpressionSet or a matrix/data.frame")
}
keep_median <- rep(TRUE, nrow(exprs_mat))
keep_sd <- rep(TRUE, nrow(exprs_mat))
if (filter_median) {
keep_median <- apply(exprs_mat, 1, median) > median_threshold
}
if (filter_sd) {
keep_sd <- apply(exprs_mat, 1, sd) > sd_threshold
}
keep <- keep_median & keep_sd
# Filter the expression matrix or ExpressionSet accordingly
if (class(expr_data)[1] == "ExpressionSet") {
filtered <- expr_data[keep, ]
} else {
filtered <- exprs_mat[keep, , drop = FALSE]
}
return(filtered)
}Code
merged_expr <- filter_low_expression(merged_expr, median_threshold = 5, sd_threshold = 0.5)
dim(merged_expr)[1] 18462 55Code
metadata <- metadata[!is.na(metadata$grade), ]
metadata sample grade batch
gse17907.GSM447197 GSM447197 Grade3 gse17907
gse17907.GSM447198 GSM447198 Grade3 gse17907
gse17907.GSM447199 GSM447199 Grade3 gse17907
gse17907.GSM447200 GSM447200 Grade3 gse17907
gse17907.GSM447201 GSM447201 Grade3 gse17907
gse17907.GSM447202 GSM447202 Grade3 gse17907
gse17907.GSM447203 GSM447203 Grade3 gse17907
gse17907.GSM447204 GSM447204 Grade3 gse17907
gse17907.GSM447205 GSM447205 Grade3 gse17907
gse17907.GSM447206 GSM447206 Grade3 gse17907
gse17907.GSM447207 GSM447207 Grade2 gse17907
gse17907.GSM447208 GSM447208 Grade2 gse17907
gse17907.GSM447209 GSM447209 Grade3 gse17907
gse17907.GSM447210 GSM447210 Grade3 gse17907
gse17907.GSM447211 GSM447211 Grade3 gse17907
gse17907.GSM447212 GSM447212 Grade3 gse17907
gse17907.GSM447213 GSM447213 Grade2 gse17907
gse17907.GSM447214 GSM447214 Grade2 gse17907
gse17907.GSM447215 GSM447215 Grade3 gse17907
gse17907.GSM447216 GSM447216 Grade3 gse17907
gse17907.GSM447217 GSM447217 Grade2 gse17907
gse17907.GSM447218 GSM447218 Grade3 gse17907
gse17907.GSM447219 GSM447219 Grade3 gse17907
gse17907.GSM447220 GSM447220 Normal gse17907
gse17907.GSM447221 GSM447221 Grade3 gse17907
gse17907.GSM447222 GSM447222 Grade2 gse17907
gse17907.GSM447223 GSM447223 Grade3 gse17907
gse17907.GSM447224 GSM447224 Grade3 gse17907
gse17907.GSM447225 GSM447225 Grade3 gse17907
gse17907.GSM447226 GSM447226 Grade3 gse17907
gse17907.GSM447227 GSM447227 Grade2 gse17907
gse17907.GSM447228 GSM447228 Grade2 gse17907
gse17907.GSM447229 GSM447229 Grade3 gse17907
gse17907.GSM447230 GSM447230 Grade3 gse17907
gse17907.GSM447231 GSM447231 Grade3 gse17907
gse17907.GSM447232 GSM447232 Grade3 gse17907
gse17907.GSM447233 GSM447233 Grade3 gse17907
gse17907.GSM447234 GSM447234 Grade3 gse17907
gse17907.GSM447235 GSM447235 Normal gse17907
gse17907.GSM447236 GSM447236 Grade3 gse17907
gse17907.GSM447237 GSM447237 Normal gse17907
gse17907.GSM447238 GSM447238 Grade1 gse17907
gse17907.GSM447239 GSM447239 Grade3 gse17907
gse17907.GSM447240 GSM447240 Grade1 gse17907
gse17907.GSM447241 GSM447241 Normal gse17907
gse17907.GSM447242 GSM447242 Grade2 gse17907
gse17907.GSM447243 GSM447243 Grade2 gse17907
gse17907.GSM447244 GSM447244 Grade1 gse17907
gse17907.GSM447245 GSM447245 Grade3 gse17907
gse17907.GSM447246 GSM447246 Grade3 gse17907
gse17907.GSM447247 GSM447247 Grade3 gse17907
gse17907.GSM447248 GSM447248 Normal gse17907
gse17907.GSM447249 GSM447249 Normal gse17907
gse17907.GSM447250 GSM447250 Normal gse17907
gse17907.GSM447251 GSM447251 Normal gse17907Code
# 🧼 Remove corresponding columns from merged_expr
gse10810 <- merged_expr[, metadata$sample]
anyNA(merged_expr)[1] FALSECode
sum(is.na(merged_expr))[1] 0Code
merged_expr <- merged_expr[complete.cases(merged_expr), ]
merged_expr GSM447197 GSM447198 GSM447199 GSM447200 GSM447201 GSM447202
1007_s_at 9.903 9.686 9.012 9.997 9.005 9.870
1053_at 6.758 6.392 6.772 6.149 5.891 6.711
117_at 6.455 5.963 6.632 7.007 7.638 4.975
1294_at 6.706 6.074 6.750 8.126 8.655 7.612
1405_i_at 6.114 5.303 6.444 9.325 10.003 7.416
1438_at 5.451 6.620 6.382 6.669 6.482 6.344
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[ reached getOption("max.print") -- omitted 16644 rows ]Code
table(metadata$grade)
Grade1 Grade2 Grade3 Normal
3 10 34 8 Code
RNGkind(kind = "L'Ecuyer-CMRG")
set.seed(123)
split_by_grade <- split(metadata, metadata$grade)
train_samples <- unlist(lapply(split_by_grade, function(df) {
n <- nrow(df)
if (n >= 2) {
sample(df$sample, size = floor(0.8 * n))
} else {
sample(df$sample, size = 1)
}
}))
test_samples <- setdiff(metadata$sample, train_samples)
# ========== 6. Extract the expression matrix and metadata ==========
expr_train <- merged_expr[, train_samples]
expr_test <- merged_expr[, test_samples]
metadata_train <- metadata[match(train_samples, metadata$sample), ]
metadata_test <- metadata[match(test_samples, metadata$sample), ]
metadata_train$grade <- factor(metadata_train$grade,
levels = c("Normal", "Grade1", "Grade2", "Grade3"))
metadata_test$grade <- factor(metadata_test$grade,
levels = c("Normal", "Grade1", "Grade2", "Grade3"))
levels(metadata_train$grade) <- make.names(levels(metadata_train$grade))
levels(metadata_test$grade) <- make.names(levels(metadata_test$grade))
cat("Training set grade counts:\n")Training set grade counts:Code
print(table(metadata_train$grade))
Normal Grade1 Grade2 Grade3
6 2 8 27 Code
cat("Test set grade counts:\n")Test set grade counts:Code
print(table(metadata_test$grade))
Normal Grade1 Grade2 Grade3
2 1 2 7 Code
expr_balanced <- expr_trainCode
# Feature Selection on SMOTE-balanced Data
labels_factor <- factor(metadata_train$grade, levels = c("Normal", "Grade1", "Grade2", "Grade3"))
design <- model.matrix(~ 0 + labels_factor)
colnames(design) <- levels(labels_factor)
fit <- lmFit(expr_balanced, design)
fit <- eBayes(fit)
res <- topTable(fit, number = Inf, adjust.method = "BH")
# Significant genes (adjusted p < 0.05)
sig_genes <- rownames(subset(res, adj.P.Val < 0.05))
# Group means and strong difference filtering (max - min > 1)
group_means <- sapply(levels(labels_factor), function(lv) {
rowMeans(expr_balanced[, metadata_train$grade == lv, drop = FALSE])
})
expr_diff <- apply(group_means, 1, function(x) max(x) - min(x))
sig_genes_strong <- sig_genes[expr_diff[sig_genes] > 1]
# Sort by adjusted p-value
res_sig_strong <- res[sig_genes_strong, ]
res_sig_strong <- res_sig_strong[order(res_sig_strong$adj.P.Val), ]
# Build top gene sets only if enough genes exist
top_gene_sets <- list()
if (nrow(res_sig_strong) >= 30) top_gene_sets$top30 <- rownames(res_sig_strong)[1:30]
if (nrow(res_sig_strong) >= 50) top_gene_sets$top50 <- rownames(res_sig_strong)[1:50]
if (nrow(res_sig_strong) >= 80) top_gene_sets$top80 <- rownames(res_sig_strong)[1:80]
if (nrow(res_sig_strong) >= 110) top_gene_sets$top110 <- rownames(res_sig_strong)[1:110]
# Optional: show available gene set sizes
cat("Available gene sets:\n")Available gene sets:Code
print(names(top_gene_sets))[1] "top30" "top50" "top80" "top110"Code
# Feature Selection on SMOTE-balanced Data
labels_factor <- factor(metadata_train$grade, levels = c("Normal", "Grade1", "Grade2", "Grade3"))
design <- model.matrix(~ 0 + labels_factor)
colnames(design) <- levels(labels_factor)
fit <- lmFit(expr_balanced, design)
fit <- eBayes(fit)
res <- topTable(fit, number = Inf, adjust.method = "BH")
# Significant genes (adjusted p < 0.05)
sig_genes <- rownames(subset(res, adj.P.Val < 0.05))
# Group means and strong difference filtering (max - min > 1)
group_means <- sapply(levels(labels_factor), function(lv) {
rowMeans(expr_balanced[, metadata_train$grade == lv, drop = FALSE])
})
expr_diff <- apply(group_means, 1, function(x) max(x) - min(x))
sig_genes_strong <- sig_genes[expr_diff[sig_genes] > 1]
# Sort by adjusted p-value
res_sig_strong <- res[sig_genes_strong, ]
res_sig_strong <- res_sig_strong[order(res_sig_strong$adj.P.Val), ]
# Build top gene sets only if enough genes exist
top_gene_sets <- list()
if (nrow(res_sig_strong) >= 30) top_gene_sets$top30 <- rownames(res_sig_strong)[1:30]
if (nrow(res_sig_strong) >= 50) top_gene_sets$top50 <- rownames(res_sig_strong)[1:50]
if (nrow(res_sig_strong) >= 80) top_gene_sets$top80 <- rownames(res_sig_strong)[1:80]
if (nrow(res_sig_strong) >= 110) top_gene_sets$top110 <- rownames(res_sig_strong)[1:110]
# Optional: show available gene set sizes
cat("Available gene sets:\n")Available gene sets:Code
print(names(top_gene_sets))[1] "top30" "top50" "top80" "top110"Code
library(caret)
library(ggplot2)
library(tidyr)
set.seed(123)
results <- list()
cv_folds <- 5
# ----------------- Create reproducible seeds -----------------
generate_seed_list <- function(num_resamples, max_tune_length) {
set.seed(123)
seed_list <- vector(mode = "list", length = num_resamples + 1)
for (i in 1:num_resamples) {
seed_list[[i]] <- sample.int(1e6, max_tune_length)
}
seed_list[[num_resamples + 1]] <- sample.int(1e6, 1)
return(seed_list)
}
svm_grid <- data.frame(C = 1)
knn_grid <- expand.grid(k = 5:10)
rf_grid <- expand.grid(mtry = floor(sqrt(ncol(expr_train))))
max_tune_length <- max(nrow(svm_grid), nrow(knn_grid), nrow(rf_grid))
seeds <- generate_seed_list(cv_folds, max_tune_length)
# ----------------- Begin training loop -----------------
y_train <- as.factor(metadata_train$grade)
levels(y_train) <- make.names(levels(y_train))
y_test <- make.names(metadata_test$grade)
acc_list <- list()
summary_list <- list()
for (gene_set_name in names(top_gene_sets)) {
gene_set <- top_gene_sets[[gene_set_name]]
x_train <- t(expr_train[gene_set, , drop = FALSE])
x_test <- t(expr_test[gene_set, , drop = FALSE])
trctrl <- trainControl(
method = "cv",
number = cv_folds,
classProbs = TRUE,
savePredictions = "final",
seeds = seeds
)
# Model training
svm_mod <- train(x = x_train, y = y_train, method = "svmLinear", trControl = trctrl, tuneGrid = svm_grid)
knn_mod <- train(x = x_train, y = y_train, method = "knn", trControl = trctrl, tuneGrid = knn_grid)
rf_mod <- train(x = x_train, y = y_train, method = "rf", trControl = trctrl, tuneGrid = rf_grid, ntree = 100)
# ---------- Save CV results ----------
acc_list[[length(acc_list) + 1]] <- data.frame(GeneSet = gene_set_name, Model = "SVM", Accuracy = svm_mod$resample$Accuracy)
acc_list[[length(acc_list) + 1]] <- data.frame(GeneSet = gene_set_name, Model = "KNN", Accuracy = knn_mod$resample$Accuracy)
acc_list[[length(acc_list) + 1]] <- data.frame(GeneSet = gene_set_name, Model = "RF", Accuracy = rf_mod$resample$Accuracy)
# ---------- Test set evaluation ----------
test_preds <- list(
SVM = predict(svm_mod, x_test),
KNN = predict(knn_mod, x_test),
RF = predict(rf_mod, x_test)
)
for (model_name in names(test_preds)) {
pred <- factor(test_preds[[model_name]], levels = unique(y_test))
truth <- factor(y_test, levels = unique(y_test))
cm <- confusionMatrix(pred, truth)
acc <- cm$overall["Accuracy"]
kappa <- cm$overall["Kappa"]
macro_f1 <- mean(cm$byClass[, "F1"], na.rm = TRUE)
macro_precision <- mean(cm$byClass[, "Precision"], na.rm = TRUE)
macro_recall <- mean(cm$byClass[, "Recall"], na.rm = TRUE)
bal_acc <- mean(cm$byClass[, "Balanced Accuracy"], na.rm = TRUE)
macro_sens <- mean(cm$byClass[, "Sensitivity"], na.rm = TRUE)
macro_spec <- mean(cm$byClass[, "Specificity"], na.rm = TRUE)
summary_list[[length(summary_list) + 1]] <- data.frame(
GeneSet = gene_set_name,
Model = model_name,
Accuracy = round(acc, 4),
MacroF1 = round(macro_f1, 4),
MacroPrecision = round(macro_precision, 4),
MacroRecall = round(macro_recall, 4),
BalancedAccuracy = round(bal_acc, 4),
MacroSensitivity = round(macro_sens, 4),
MacroSpecificity = round(macro_spec, 4),
Kappa = round(kappa, 4)
)
cat("\n======", gene_set_name, "-", model_name, "======\n")
print(cm$table)
cat("Test Accuracy:", round(acc, 3),
"| Kappa:", round(kappa, 3),
"| Macro F1:", round(macro_f1, 3),
"| Precision:", round(macro_precision, 3),
"| Recall:", round(macro_recall, 3),
"| Sensitivity:", round(macro_sens, 3),
"| Specificity:", round(macro_spec, 3),
"| Balanced Accuracy:", round(bal_acc, 3), "\n")
}
}Warning: model fit failed for Fold2: C=1 Error in indexes[[j]] : subscript out of boundsWarning: model fit failed for Fold5: C=1 Error in indexes[[j]] : subscript out of boundsWarning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
: There were missing values in resampled performance measures.
====== top30 - SVM ======
Reference
Prediction Grade3 Grade2 Normal Grade1
Grade3 7 2 2 1
Grade2 0 0 0 0
Normal 0 0 0 0
Grade1 0 0 0 0
Test Accuracy: 0.583 | Kappa: 0 | Macro F1: 0.737 | Precision: 0.583 | Recall: 0.25 | Sensitivity: 0.25 | Specificity: 0.75 | Balanced Accuracy: 0.5
====== top30 - KNN ======
Reference
Prediction Grade3 Grade2 Normal Grade1
Grade3 7 2 1 1
Grade2 0 0 0 0
Normal 0 0 1 0
Grade1 0 0 0 0
Test Accuracy: 0.667 | Kappa: 0.262 | Macro F1: 0.722 | Precision: 0.818 | Recall: 0.375 | Sensitivity: 0.375 | Specificity: 0.8 | Balanced Accuracy: 0.588
====== top30 - RF ======
Reference
Prediction Grade3 Grade2 Normal Grade1
Grade3 7 2 1 1
Grade2 0 0 0 0
Normal 0 0 1 0
Grade1 0 0 0 0
Test Accuracy: 0.667 | Kappa: 0.262 | Macro F1: 0.722 | Precision: 0.818 | Recall: 0.375 | Sensitivity: 0.375 | Specificity: 0.8 | Balanced Accuracy: 0.588 Warning: model fit failed for Fold2: C=1 Error in indexes[[j]] : subscript out of boundsWarning: model fit failed for Fold3: C=1 Error in indexes[[j]] : subscript out of boundsWarning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
: There were missing values in resampled performance measures.
====== top50 - SVM ======
Reference
Prediction Grade3 Grade2 Normal Grade1
Grade3 5 2 1 1
Grade2 2 0 0 0
Normal 0 0 1 0
Grade1 0 0 0 0
Test Accuracy: 0.5 | Kappa: 0.04 | Macro F1: 0.646 | Precision: 0.519 | Recall: 0.304 | Sensitivity: 0.304 | Specificity: 0.75 | Balanced Accuracy: 0.527
====== top50 - KNN ======
Reference
Prediction Grade3 Grade2 Normal Grade1
Grade3 7 2 1 1
Grade2 0 0 0 0
Normal 0 0 1 0
Grade1 0 0 0 0
Test Accuracy: 0.667 | Kappa: 0.262 | Macro F1: 0.722 | Precision: 0.818 | Recall: 0.375 | Sensitivity: 0.375 | Specificity: 0.8 | Balanced Accuracy: 0.588
====== top50 - RF ======
Reference
Prediction Grade3 Grade2 Normal Grade1
Grade3 7 2 1 1
Grade2 0 0 0 0
Normal 0 0 1 0
Grade1 0 0 0 0
Test Accuracy: 0.667 | Kappa: 0.262 | Macro F1: 0.722 | Precision: 0.818 | Recall: 0.375 | Sensitivity: 0.375 | Specificity: 0.8 | Balanced Accuracy: 0.588 Warning: model fit failed for Fold2: C=1 Error in indexes[[j]] : subscript out of boundsWarning: model fit failed for Fold5: C=1 Error in indexes[[j]] : subscript out of boundsWarning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
: There were missing values in resampled performance measures.
====== top80 - SVM ======
Reference
Prediction Grade3 Grade2 Normal Grade1
Grade3 7 2 2 1
Grade2 0 0 0 0
Normal 0 0 0 0
Grade1 0 0 0 0
Test Accuracy: 0.583 | Kappa: 0 | Macro F1: 0.737 | Precision: 0.583 | Recall: 0.25 | Sensitivity: 0.25 | Specificity: 0.75 | Balanced Accuracy: 0.5
====== top80 - KNN ======
Reference
Prediction Grade3 Grade2 Normal Grade1
Grade3 6 2 1 1
Grade2 1 0 0 0
Normal 0 0 1 0
Grade1 0 0 0 0
Test Accuracy: 0.583 | Kappa: 0.143 | Macro F1: 0.686 | Precision: 0.533 | Recall: 0.339 | Sensitivity: 0.339 | Specificity: 0.775 | Balanced Accuracy: 0.557
====== top80 - RF ======
Reference
Prediction Grade3 Grade2 Normal Grade1
Grade3 7 2 1 1
Grade2 0 0 0 0
Normal 0 0 1 0
Grade1 0 0 0 0
Test Accuracy: 0.667 | Kappa: 0.262 | Macro F1: 0.722 | Precision: 0.818 | Recall: 0.375 | Sensitivity: 0.375 | Specificity: 0.8 | Balanced Accuracy: 0.588 Warning: model fit failed for Fold1: C=1 Error in indexes[[j]] : subscript out of boundsWarning: model fit failed for Fold2: C=1 Error in indexes[[j]] : subscript out of boundsWarning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
: There were missing values in resampled performance measures.
====== top110 - SVM ======
Reference
Prediction Grade3 Grade2 Normal Grade1
Grade3 7 2 2 1
Grade2 0 0 0 0
Normal 0 0 0 0
Grade1 0 0 0 0
Test Accuracy: 0.583 | Kappa: 0 | Macro F1: 0.737 | Precision: 0.583 | Recall: 0.25 | Sensitivity: 0.25 | Specificity: 0.75 | Balanced Accuracy: 0.5
====== top110 - KNN ======
Reference
Prediction Grade3 Grade2 Normal Grade1
Grade3 7 2 1 1
Grade2 0 0 0 0
Normal 0 0 1 0
Grade1 0 0 0 0
Test Accuracy: 0.667 | Kappa: 0.262 | Macro F1: 0.722 | Precision: 0.818 | Recall: 0.375 | Sensitivity: 0.375 | Specificity: 0.8 | Balanced Accuracy: 0.588
====== top110 - RF ======
Reference
Prediction Grade3 Grade2 Normal Grade1
Grade3 7 2 1 1
Grade2 0 0 0 0
Normal 0 0 1 0
Grade1 0 0 0 0
Test Accuracy: 0.667 | Kappa: 0.262 | Macro F1: 0.722 | Precision: 0.818 | Recall: 0.375 | Sensitivity: 0.375 | Specificity: 0.8 | Balanced Accuracy: 0.588 Code
acc_df <- do.call(rbind, acc_list)
summary_df <- do.call(rbind, summary_list)
desired_order <- c("top30", "top50", "top80", "top110")
acc_df$GeneSet <- factor(acc_df$GeneSet, levels = desired_order)
summary_df$GeneSet <- factor(summary_df$GeneSet, levels = desired_order)
# -------- Plot the CV accuracy boxplot --------
gse17907_cv_plot <- ggplot(acc_df, aes(x = GeneSet, y = Accuracy, fill = Model)) +
geom_boxplot(position = position_dodge(0.8), outlier.shape = NA) +
geom_jitter(position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8), size = 1, alpha = 0.6) +
labs(
title = "Model Accuracy (Linear selection)",
x = "Top Gene Set",
y = "Cross-validated Accuracy"
) +
theme_minimal(base_size = 13) +
theme(legend.position = "top")
# -------- Plot the Test evaluation metric bar chart --------
eval_long_gse17907 <- pivot_longer(summary_df,
cols = c("Accuracy", "MacroF1", "MacroPrecision", "MacroRecall", "MacroSensitivity", "MacroSpecificity", "BalancedAccuracy", "Kappa"),
names_to = "Metric", values_to = "Value")
gse17907_eval_plot <- ggplot(eval_long_gse17907 , aes(x = GeneSet, y = Value, fill = Model)) +
geom_col(position = position_dodge(0.8)) +
facet_wrap(~ Metric, scales = "free_y") +
labs(
title = "Test Set Evaluation: Extended Metrics",
x = "Top Gene Set",
y = "Metric"
) +
theme_minimal(base_size = 13) +
theme(legend.position = "top")
gse17907_cv_plotWarning: Removed 8 rows containing non-finite outside the scale range
(`stat_boxplot()`).Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).
Code
gse17907_eval_plot
8.1.3 dataframe of result for gse17907
Code
gse17907_metrics<- tidyr::pivot_wider(
eval_long_gse17907,
names_from = Metric,
values_from = Value
)
gse17907_metrics# A tibble: 12 × 10
GeneSet Model Accuracy MacroF1 MacroPrecision MacroRecall MacroSensitivity
<fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 top30 SVM 0.583 0.737 0.583 0.25 0.25
2 top30 KNN 0.667 0.722 0.818 0.375 0.375
3 top30 RF 0.667 0.722 0.818 0.375 0.375
4 top50 SVM 0.5 0.646 0.518 0.304 0.304
5 top50 KNN 0.667 0.722 0.818 0.375 0.375
6 top50 RF 0.667 0.722 0.818 0.375 0.375
7 top80 SVM 0.583 0.737 0.583 0.25 0.25
8 top80 KNN 0.583 0.686 0.533 0.339 0.339
9 top80 RF 0.667 0.722 0.818 0.375 0.375
10 top110 SVM 0.583 0.737 0.583 0.25 0.25
11 top110 KNN 0.667 0.722 0.818 0.375 0.375
12 top110 RF 0.667 0.722 0.818 0.375 0.375
# ℹ 3 more variables: MacroSpecificity <dbl>, BalancedAccuracy <dbl>,
# Kappa <dbl>Code
gse17907_metrics$Config <- paste(gse17907_metrics$Model, gse17907_metrics$GeneSet, sep = "_")
# Remove duplicated rows just in case (optional but safe)
gse17907_metrics <- distinct(gse17907_metrics)
# Select top 5 *distinct* models by Balanced Accuracy
top5 <- gse17907_metrics %>%
arrange(desc(BalancedAccuracy)) %>%
slice_head(n = 5)
# Reshape to long format for heatmap
top5_long <- melt(top5, id.vars = c("Model", "GeneSet", "Config"))
# Plot heatmap
gse17907_heat <- ggplot(top5_long, aes(x = variable, y = Config, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient(low = "white", high = "#d07095", name = "Metric Value") +
labs(title = "Performance Heatmap of Top 5 Models",
x = "Metric",
y = "Model (Config)") +
theme_minimal(base_size = 14) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
gse17907_heat
8.1.4 gse15852 code
Code
# 1. Expression sets
expr_list <- list(
gse15852 = exprs(gse15852)
)
# 2. Phenotype sets
pdata_list <- list(
pdata15852
)
# 3. Fix phenotype rownames using geo_accession if present
pdata_list <- lapply(pdata_list, function(p) {
if ("geo_accession" %in% colnames(p)) {
rownames(p) <- p$geo_accession
}
return(p)
})
# Reassign corrected pdata
# 1. Assign pdata
pdata15852 <- pdata_list[[1]]
# 2. Create pheno_list with dataset names
pheno_list <- list(
gse15852 = pdata15852
)
# 3. Extract grade list and track batch
grade_list <- Map(function(expr, pheno, batch_name) {
sample_names <- colnames(expr)
matched_pheno <- pheno[sample_names, , drop = FALSE]
grades <- matched_pheno$grade
names(grades) <- sample_names
# Track batch
data.frame(
sample = sample_names,
grade = grades,
batch = batch_name,
stringsAsFactors = FALSE
)
}, expr_list, pheno_list, names(pheno_list))
# 4. Combine all grade + batch info into metadata
metadata <- do.call(rbind, grade_list)
# 5. Clean sample names to remove prefixes (e.g., "gse10810.")
metadata$sample <- sub(".*\\.", "", metadata$sample)
# 6. Combine expression matrices
expr_dfs <- lapply(expr_list, function(mat) {
df <- as.data.frame(mat)
df$gene <- rownames(mat)
df
})
merged_expr <- Reduce(function(x, y) full_join(x, y, by = "gene"), expr_dfs)
rownames(merged_expr) <- merged_expr$gene
merged_expr$gene <- NULL
# 7. Ensure metadata matches expression matrix columns
colnames(merged_expr) <- sub(".*\\.", "", colnames(merged_expr)) # Match cleaned sample names
metadata <- metadata[match(colnames(merged_expr), metadata$sample), ]
# 8. Final check
stopifnot(all(metadata$sample == colnames(merged_expr)))
# ✅ Outputs
metadata sample grade batch
gse15852.GSM398074 GSM398074 Normal gse15852
gse15852.GSM398075 GSM398075 Grade2 gse15852
gse15852.GSM398076 GSM398076 Normal gse15852
gse15852.GSM398077 GSM398077 Grade2 gse15852
gse15852.GSM398078 GSM398078 Normal gse15852
gse15852.GSM398079 GSM398079 Grade3 gse15852
gse15852.GSM398080 GSM398080 Normal gse15852
gse15852.GSM398081 GSM398081 Grade1 gse15852
gse15852.GSM398082 GSM398082 Normal gse15852
gse15852.GSM398083 GSM398083 Grade3 gse15852
gse15852.GSM398084 GSM398084 Normal gse15852
gse15852.GSM398085 GSM398085 Grade2 gse15852
gse15852.GSM398086 GSM398086 Normal gse15852
gse15852.GSM398087 GSM398087 Grade3 gse15852
gse15852.GSM398088 GSM398088 Normal gse15852
gse15852.GSM398089 GSM398089 Grade2 gse15852
gse15852.GSM398090 GSM398090 Normal gse15852
gse15852.GSM398091 GSM398091 Grade1 gse15852
gse15852.GSM398092 GSM398092 Normal gse15852
gse15852.GSM398093 GSM398093 Grade3 gse15852
gse15852.GSM398094 GSM398094 Normal gse15852
gse15852.GSM398095 GSM398095 Grade1 gse15852
gse15852.GSM398096 GSM398096 Normal gse15852
gse15852.GSM398097 GSM398097 Grade2 gse15852
gse15852.GSM398098 GSM398098 Normal gse15852
gse15852.GSM398099 GSM398099 Grade3 gse15852
gse15852.GSM398100 GSM398100 Normal gse15852
gse15852.GSM398101 GSM398101 Grade2 gse15852
gse15852.GSM398102 GSM398102 Normal gse15852
gse15852.GSM398103 GSM398103 Grade2 gse15852
gse15852.GSM398104 GSM398104 Normal gse15852
gse15852.GSM398105 GSM398105 Grade1 gse15852
gse15852.GSM398106 GSM398106 Normal gse15852
gse15852.GSM398107 GSM398107 Grade2 gse15852
gse15852.GSM398108 GSM398108 Normal gse15852
gse15852.GSM398109 GSM398109 Grade1 gse15852
gse15852.GSM398110 GSM398110 Normal gse15852
gse15852.GSM398111 GSM398111 Grade3 gse15852
gse15852.GSM398112 GSM398112 Normal gse15852
gse15852.GSM398113 GSM398113 Grade3 gse15852
gse15852.GSM398114 GSM398114 Normal gse15852
gse15852.GSM398115 GSM398115 Grade1 gse15852
gse15852.GSM398116 GSM398116 Normal gse15852
gse15852.GSM398117 GSM398117 Grade3 gse15852
gse15852.GSM398118 GSM398118 Normal gse15852
gse15852.GSM398119 GSM398119 Grade2 gse15852
gse15852.GSM398120 GSM398120 Normal gse15852
gse15852.GSM398121 GSM398121 Grade2 gse15852
gse15852.GSM398122 GSM398122 Normal gse15852
gse15852.GSM398123 GSM398123 Grade2 gse15852
gse15852.GSM398124 GSM398124 Normal gse15852
gse15852.GSM398125 GSM398125 Grade2 gse15852
gse15852.GSM398126 GSM398126 Normal gse15852
gse15852.GSM398127 GSM398127 Grade2 gse15852
gse15852.GSM398128 GSM398128 Normal gse15852
gse15852.GSM398129 GSM398129 Grade2 gse15852
gse15852.GSM398130 GSM398130 Normal gse15852
gse15852.GSM398131 GSM398131 Grade2 gse15852
gse15852.GSM398132 GSM398132 Normal gse15852
gse15852.GSM398133 GSM398133 Grade2 gse15852
gse15852.GSM398134 GSM398134 Normal gse15852
gse15852.GSM398135 GSM398135 Grade2 gse15852
gse15852.GSM398136 GSM398136 Normal gse15852
gse15852.GSM398137 GSM398137 Grade2 gse15852
gse15852.GSM398138 GSM398138 Normal gse15852
gse15852.GSM398139 GSM398139 Grade2 gse15852
gse15852.GSM398140 GSM398140 Normal gse15852
gse15852.GSM398141 GSM398141 Grade3 gse15852
gse15852.GSM398142 GSM398142 Normal gse15852
gse15852.GSM398143 GSM398143 Grade3 gse15852
gse15852.GSM398144 GSM398144 Normal gse15852
gse15852.GSM398145 GSM398145 Grade2 gse15852
gse15852.GSM398146 GSM398146 Normal gse15852
gse15852.GSM398147 GSM398147 Grade2 gse15852
gse15852.GSM398148 GSM398148 Normal gse15852
gse15852.GSM398149 GSM398149 Grade3 gse15852
gse15852.GSM398150 GSM398150 Normal gse15852
gse15852.GSM398151 GSM398151 Grade1 gse15852
gse15852.GSM398152 GSM398152 Normal gse15852
gse15852.GSM398153 GSM398153 Grade2 gse15852
gse15852.GSM398154 GSM398154 Normal gse15852
gse15852.GSM398155 GSM398155 Grade2 gse15852
gse15852.GSM398156 GSM398156 Normal gse15852
gse15852.GSM398157 GSM398157 Grade3 gse15852
gse15852.GSM398158 GSM398158 Normal gse15852
gse15852.GSM398159 GSM398159 Grade1 gse15852Code
filter_low_expression <- function(expr_data, median_threshold = 5, sd_threshold = 0.5, filter_median = TRUE, filter_sd = TRUE) {
# expr_data: expression matrix (genes x samples) or ExpressionSet object
# median_threshold: genes with median expression below this will be filtered out
# sd_threshold: genes with SD below this will be filtered out
# filter_median: whether to filter by median expression
# filter_sd: whether to filter by standard deviation (variance)
# If input is ExpressionSet, extract expression matrix
if (class(expr_data)[1] == "ExpressionSet") {
exprs_mat <- exprs(expr_data)
} else if (is.matrix(expr_data) || is.data.frame(expr_data)) {
exprs_mat <- as.matrix(expr_data)
} else {
stop("expr_data must be an ExpressionSet or a matrix/data.frame")
}
keep_median <- rep(TRUE, nrow(exprs_mat))
keep_sd <- rep(TRUE, nrow(exprs_mat))
if (filter_median) {
keep_median <- apply(exprs_mat, 1, median) > median_threshold
}
if (filter_sd) {
keep_sd <- apply(exprs_mat, 1, sd) > sd_threshold
}
keep <- keep_median & keep_sd
# Filter the expression matrix or ExpressionSet accordingly
if (class(expr_data)[1] == "ExpressionSet") {
filtered <- expr_data[keep, ]
} else {
filtered <- exprs_mat[keep, , drop = FALSE]
}
return(filtered)
}Code
merged_expr <- filter_low_expression(merged_expr, median_threshold = 5, sd_threshold = 0.5)
dim(merged_expr)[1] 22282 86Code
metadata <- metadata[!is.na(metadata$grade), ]
metadata sample grade batch
gse15852.GSM398074 GSM398074 Normal gse15852
gse15852.GSM398075 GSM398075 Grade2 gse15852
gse15852.GSM398076 GSM398076 Normal gse15852
gse15852.GSM398077 GSM398077 Grade2 gse15852
gse15852.GSM398078 GSM398078 Normal gse15852
gse15852.GSM398079 GSM398079 Grade3 gse15852
gse15852.GSM398080 GSM398080 Normal gse15852
gse15852.GSM398081 GSM398081 Grade1 gse15852
gse15852.GSM398082 GSM398082 Normal gse15852
gse15852.GSM398083 GSM398083 Grade3 gse15852
gse15852.GSM398084 GSM398084 Normal gse15852
gse15852.GSM398085 GSM398085 Grade2 gse15852
gse15852.GSM398086 GSM398086 Normal gse15852
gse15852.GSM398087 GSM398087 Grade3 gse15852
gse15852.GSM398088 GSM398088 Normal gse15852
gse15852.GSM398089 GSM398089 Grade2 gse15852
gse15852.GSM398090 GSM398090 Normal gse15852
gse15852.GSM398091 GSM398091 Grade1 gse15852
gse15852.GSM398092 GSM398092 Normal gse15852
gse15852.GSM398093 GSM398093 Grade3 gse15852
gse15852.GSM398094 GSM398094 Normal gse15852
gse15852.GSM398095 GSM398095 Grade1 gse15852
gse15852.GSM398096 GSM398096 Normal gse15852
gse15852.GSM398097 GSM398097 Grade2 gse15852
gse15852.GSM398098 GSM398098 Normal gse15852
gse15852.GSM398099 GSM398099 Grade3 gse15852
gse15852.GSM398100 GSM398100 Normal gse15852
gse15852.GSM398101 GSM398101 Grade2 gse15852
gse15852.GSM398102 GSM398102 Normal gse15852
gse15852.GSM398103 GSM398103 Grade2 gse15852
gse15852.GSM398104 GSM398104 Normal gse15852
gse15852.GSM398105 GSM398105 Grade1 gse15852
gse15852.GSM398106 GSM398106 Normal gse15852
gse15852.GSM398107 GSM398107 Grade2 gse15852
gse15852.GSM398108 GSM398108 Normal gse15852
gse15852.GSM398109 GSM398109 Grade1 gse15852
gse15852.GSM398110 GSM398110 Normal gse15852
gse15852.GSM398111 GSM398111 Grade3 gse15852
gse15852.GSM398112 GSM398112 Normal gse15852
gse15852.GSM398113 GSM398113 Grade3 gse15852
gse15852.GSM398114 GSM398114 Normal gse15852
gse15852.GSM398115 GSM398115 Grade1 gse15852
gse15852.GSM398116 GSM398116 Normal gse15852
gse15852.GSM398117 GSM398117 Grade3 gse15852
gse15852.GSM398118 GSM398118 Normal gse15852
gse15852.GSM398119 GSM398119 Grade2 gse15852
gse15852.GSM398120 GSM398120 Normal gse15852
gse15852.GSM398121 GSM398121 Grade2 gse15852
gse15852.GSM398122 GSM398122 Normal gse15852
gse15852.GSM398123 GSM398123 Grade2 gse15852
gse15852.GSM398124 GSM398124 Normal gse15852
gse15852.GSM398125 GSM398125 Grade2 gse15852
gse15852.GSM398126 GSM398126 Normal gse15852
gse15852.GSM398127 GSM398127 Grade2 gse15852
gse15852.GSM398128 GSM398128 Normal gse15852
gse15852.GSM398129 GSM398129 Grade2 gse15852
gse15852.GSM398130 GSM398130 Normal gse15852
gse15852.GSM398131 GSM398131 Grade2 gse15852
gse15852.GSM398132 GSM398132 Normal gse15852
gse15852.GSM398133 GSM398133 Grade2 gse15852
gse15852.GSM398134 GSM398134 Normal gse15852
gse15852.GSM398135 GSM398135 Grade2 gse15852
gse15852.GSM398136 GSM398136 Normal gse15852
gse15852.GSM398137 GSM398137 Grade2 gse15852
gse15852.GSM398138 GSM398138 Normal gse15852
gse15852.GSM398139 GSM398139 Grade2 gse15852
gse15852.GSM398140 GSM398140 Normal gse15852
gse15852.GSM398141 GSM398141 Grade3 gse15852
gse15852.GSM398142 GSM398142 Normal gse15852
gse15852.GSM398143 GSM398143 Grade3 gse15852
gse15852.GSM398144 GSM398144 Normal gse15852
gse15852.GSM398145 GSM398145 Grade2 gse15852
gse15852.GSM398146 GSM398146 Normal gse15852
gse15852.GSM398147 GSM398147 Grade2 gse15852
gse15852.GSM398148 GSM398148 Normal gse15852
gse15852.GSM398149 GSM398149 Grade3 gse15852
gse15852.GSM398150 GSM398150 Normal gse15852
gse15852.GSM398151 GSM398151 Grade1 gse15852
gse15852.GSM398152 GSM398152 Normal gse15852
gse15852.GSM398153 GSM398153 Grade2 gse15852
gse15852.GSM398154 GSM398154 Normal gse15852
gse15852.GSM398155 GSM398155 Grade2 gse15852
gse15852.GSM398156 GSM398156 Normal gse15852
gse15852.GSM398157 GSM398157 Grade3 gse15852
gse15852.GSM398158 GSM398158 Normal gse15852
gse15852.GSM398159 GSM398159 Grade1 gse15852Code
# 🧼 Remove corresponding columns from merged_expr
gse10810 <- merged_expr[, metadata$sample]
anyNA(merged_expr)[1] FALSECode
sum(is.na(merged_expr))[1] 0Code
merged_expr <- merged_expr[complete.cases(merged_expr), ]
merged_expr GSM398074 GSM398075 GSM398076 GSM398077
1007_s_at 1881.80000 2317.51000 1553.86000 1915.57000
1053_at 78.06580 61.35400 80.05250 79.85180
117_at 1299.98000 775.54700 1103.74000 762.00500
121_at 3086.72000 2335.15000 3139.65000 2338.48000
1255_g_at 353.89000 303.65300 523.87300 222.55200
1294_at 248.63500 356.76600 297.72900 414.28400
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[ reached getOption("max.print") -- omitted 21120 rows ]Code
table(metadata$grade)
Grade1 Grade2 Grade3 Normal
8 23 12 43 Code
RNGkind(kind = "L'Ecuyer-CMRG")
set.seed(123)
split_by_grade <- split(metadata, metadata$grade)
train_samples <- unlist(lapply(split_by_grade, function(df) {
n <- nrow(df)
if (n >= 2) {
sample(df$sample, size = floor(0.8 * n))
} else {
sample(df$sample, size = 1)
}
}))
test_samples <- setdiff(metadata$sample, train_samples)
# ========== 6. Extract the expression matrix and metadata ==========
expr_train <- merged_expr[, train_samples]
expr_test <- merged_expr[, test_samples]
metadata_train <- metadata[match(train_samples, metadata$sample), ]
metadata_test <- metadata[match(test_samples, metadata$sample), ]
metadata_train$grade <- factor(metadata_train$grade,
levels = c("Normal", "Grade1", "Grade2", "Grade3"))
metadata_test$grade <- factor(metadata_test$grade,
levels = c("Normal", "Grade1", "Grade2", "Grade3"))
levels(metadata_train$grade) <- make.names(levels(metadata_train$grade))
levels(metadata_test$grade) <- make.names(levels(metadata_test$grade))
cat("Training set grade counts:\n")Training set grade counts:Code
print(table(metadata_train$grade))
Normal Grade1 Grade2 Grade3
34 6 18 9 Code
cat("Test set grade counts:\n")Test set grade counts:Code
print(table(metadata_test$grade))
Normal Grade1 Grade2 Grade3
9 2 5 3 Code
expr_balanced <- expr_trainCode
# Feature Selection on SMOTE-balanced Data
labels_factor <- factor(metadata_train$grade, levels = c("Normal", "Grade1", "Grade2", "Grade3"))
design <- model.matrix(~ 0 + labels_factor)
colnames(design) <- levels(labels_factor)
fit <- lmFit(expr_balanced, design)
fit <- eBayes(fit)
res <- topTable(fit, number = Inf, adjust.method = "BH")
# Significant genes (adjusted p < 0.05)
sig_genes <- rownames(subset(res, adj.P.Val < 0.05))
# Group means and strong difference filtering (max - min > 1)
group_means <- sapply(levels(labels_factor), function(lv) {
rowMeans(expr_balanced[, metadata_train$grade == lv, drop = FALSE])
})
expr_diff <- apply(group_means, 1, function(x) max(x) - min(x))
sig_genes_strong <- sig_genes[expr_diff[sig_genes] > 1]
# Sort by adjusted p-value
res_sig_strong <- res[sig_genes_strong, ]
res_sig_strong <- res_sig_strong[order(res_sig_strong$adj.P.Val), ]
# Build top gene sets only if enough genes exist
top_gene_sets <- list()
if (nrow(res_sig_strong) >= 30) top_gene_sets$top30 <- rownames(res_sig_strong)[1:30]
if (nrow(res_sig_strong) >= 50) top_gene_sets$top50 <- rownames(res_sig_strong)[1:50]
if (nrow(res_sig_strong) >= 80) top_gene_sets$top80 <- rownames(res_sig_strong)[1:80]
if (nrow(res_sig_strong) >= 110) top_gene_sets$top110 <- rownames(res_sig_strong)[1:110]
# Optional: show available gene set sizes
cat("Available gene sets:\n")Available gene sets:Code
print(names(top_gene_sets))[1] "top30" "top50" "top80" "top110"Code
# Feature Selection on SMOTE-balanced Data
labels_factor <- factor(metadata_train$grade, levels = c("Normal", "Grade1", "Grade2", "Grade3"))
design <- model.matrix(~ 0 + labels_factor)
colnames(design) <- levels(labels_factor)
fit <- lmFit(expr_balanced, design)
fit <- eBayes(fit)
res <- topTable(fit, number = Inf, adjust.method = "BH")
# Significant genes (adjusted p < 0.05)
sig_genes <- rownames(subset(res, adj.P.Val < 0.05))
# Group means and strong difference filtering (max - min > 1)
group_means <- sapply(levels(labels_factor), function(lv) {
rowMeans(expr_balanced[, metadata_train$grade == lv, drop = FALSE])
})
expr_diff <- apply(group_means, 1, function(x) max(x) - min(x))
sig_genes_strong <- sig_genes[expr_diff[sig_genes] > 1]
# Sort by adjusted p-value
res_sig_strong <- res[sig_genes_strong, ]
res_sig_strong <- res_sig_strong[order(res_sig_strong$adj.P.Val), ]
# Build top gene sets only if enough genes exist
top_gene_sets <- list()
if (nrow(res_sig_strong) >= 30) top_gene_sets$top30 <- rownames(res_sig_strong)[1:30]
if (nrow(res_sig_strong) >= 50) top_gene_sets$top50 <- rownames(res_sig_strong)[1:50]
if (nrow(res_sig_strong) >= 80) top_gene_sets$top80 <- rownames(res_sig_strong)[1:80]
if (nrow(res_sig_strong) >= 110) top_gene_sets$top110 <- rownames(res_sig_strong)[1:110]
# Optional: show available gene set sizes
cat("Available gene sets:\n")Available gene sets:Code
print(names(top_gene_sets))[1] "top30" "top50" "top80" "top110"Code
library(caret)
library(ggplot2)
library(tidyr)
set.seed(123)
results <- list()
cv_folds <- 5
# ----------------- Create reproducible seeds -----------------
generate_seed_list <- function(num_resamples, max_tune_length) {
set.seed(123)
seed_list <- vector(mode = "list", length = num_resamples + 1)
for (i in 1:num_resamples) {
seed_list[[i]] <- sample.int(1e6, max_tune_length)
}
seed_list[[num_resamples + 1]] <- sample.int(1e6, 1)
return(seed_list)
}
svm_grid <- data.frame(C = 1)
knn_grid <- expand.grid(k = 5:10)
rf_grid <- expand.grid(mtry = floor(sqrt(ncol(expr_train))))
max_tune_length <- max(nrow(svm_grid), nrow(knn_grid), nrow(rf_grid))
seeds <- generate_seed_list(cv_folds, max_tune_length)
# ----------------- Begin training loop -----------------
y_train <- as.factor(metadata_train$grade)
levels(y_train) <- make.names(levels(y_train))
y_test <- make.names(metadata_test$grade)
acc_list <- list()
summary_list <- list()
for (gene_set_name in names(top_gene_sets)) {
gene_set <- top_gene_sets[[gene_set_name]]
x_train <- t(expr_train[gene_set, , drop = FALSE])
x_test <- t(expr_test[gene_set, , drop = FALSE])
trctrl <- trainControl(
method = "cv",
number = cv_folds,
classProbs = TRUE,
savePredictions = "final",
seeds = seeds
)
# Model training
svm_mod <- train(x = x_train, y = y_train, method = "svmLinear", trControl = trctrl, tuneGrid = svm_grid)
knn_mod <- train(x = x_train, y = y_train, method = "knn", trControl = trctrl, tuneGrid = knn_grid)
rf_mod <- train(x = x_train, y = y_train, method = "rf", trControl = trctrl, tuneGrid = rf_grid, ntree = 100)
# ---------- Save CV results ----------
acc_list[[length(acc_list) + 1]] <- data.frame(GeneSet = gene_set_name, Model = "SVM", Accuracy = svm_mod$resample$Accuracy)
acc_list[[length(acc_list) + 1]] <- data.frame(GeneSet = gene_set_name, Model = "KNN", Accuracy = knn_mod$resample$Accuracy)
acc_list[[length(acc_list) + 1]] <- data.frame(GeneSet = gene_set_name, Model = "RF", Accuracy = rf_mod$resample$Accuracy)
# ---------- Test set evaluation ----------
test_preds <- list(
SVM = predict(svm_mod, x_test),
KNN = predict(knn_mod, x_test),
RF = predict(rf_mod, x_test)
)
for (model_name in names(test_preds)) {
pred <- factor(test_preds[[model_name]], levels = unique(y_test))
truth <- factor(y_test, levels = unique(y_test))
cm <- confusionMatrix(pred, truth)
acc <- cm$overall["Accuracy"]
kappa <- cm$overall["Kappa"]
macro_f1 <- mean(cm$byClass[, "F1"], na.rm = TRUE)
macro_precision <- mean(cm$byClass[, "Precision"], na.rm = TRUE)
macro_recall <- mean(cm$byClass[, "Recall"], na.rm = TRUE)
bal_acc <- mean(cm$byClass[, "Balanced Accuracy"], na.rm = TRUE)
macro_sens <- mean(cm$byClass[, "Sensitivity"], na.rm = TRUE)
macro_spec <- mean(cm$byClass[, "Specificity"], na.rm = TRUE)
summary_list[[length(summary_list) + 1]] <- data.frame(
GeneSet = gene_set_name,
Model = model_name,
Accuracy = round(acc, 4),
MacroF1 = round(macro_f1, 4),
MacroPrecision = round(macro_precision, 4),
MacroRecall = round(macro_recall, 4),
BalancedAccuracy = round(bal_acc, 4),
MacroSensitivity = round(macro_sens, 4),
MacroSpecificity = round(macro_spec, 4),
Kappa = round(kappa, 4)
)
cat("\n======", gene_set_name, "-", model_name, "======\n")
print(cm$table)
cat("Test Accuracy:", round(acc, 3),
"| Kappa:", round(kappa, 3),
"| Macro F1:", round(macro_f1, 3),
"| Precision:", round(macro_precision, 3),
"| Recall:", round(macro_recall, 3),
"| Sensitivity:", round(macro_sens, 3),
"| Specificity:", round(macro_spec, 3),
"| Balanced Accuracy:", round(bal_acc, 3), "\n")
}
}
====== top30 - SVM ======
Reference
Prediction Grade3 Normal Grade2 Grade1
Grade3 0 0 0 0
Normal 3 9 5 2
Grade2 0 0 0 0
Grade1 0 0 0 0
Test Accuracy: 0.474 | Kappa: 0 | Macro F1: 0.643 | Precision: 0.474 | Recall: 0.25 | Sensitivity: 0.25 | Specificity: 0.75 | Balanced Accuracy: 0.5
====== top30 - KNN ======
Reference
Prediction Grade3 Normal Grade2 Grade1
Grade3 1 0 0 0
Normal 1 9 5 1
Grade2 1 0 0 1
Grade1 0 0 0 0
Test Accuracy: 0.526 | Kappa: 0.162 | Macro F1: 0.61 | Precision: 0.521 | Recall: 0.333 | Sensitivity: 0.333 | Specificity: 0.789 | Balanced Accuracy: 0.561
====== top30 - RF ======
Reference
Prediction Grade3 Normal Grade2 Grade1
Grade3 0 0 0 0
Normal 3 8 3 1
Grade2 0 1 2 1
Grade1 0 0 0 0
Test Accuracy: 0.526 | Kappa: 0.17 | Macro F1: 0.556 | Precision: 0.517 | Recall: 0.322 | Sensitivity: 0.322 | Specificity: 0.789 | Balanced Accuracy: 0.556
====== top50 - SVM ======
Reference
Prediction Grade3 Normal Grade2 Grade1
Grade3 0 0 1 0
Normal 3 9 4 2
Grade2 0 0 0 0
Grade1 0 0 0 0
Test Accuracy: 0.474 | Kappa: 0.031 | Macro F1: 0.667 | Precision: 0.25 | Recall: 0.25 | Sensitivity: 0.25 | Specificity: 0.759 | Balanced Accuracy: 0.505
====== top50 - KNN ======
Reference
Prediction Grade3 Normal Grade2 Grade1
Grade3 0 1 0 0
Normal 2 8 5 2
Grade2 1 0 0 0
Grade1 0 0 0 0
Test Accuracy: 0.421 | Kappa: -0.045 | Macro F1: 0.615 | Precision: 0.157 | Recall: 0.222 | Sensitivity: 0.222 | Specificity: 0.742 | Balanced Accuracy: 0.482
====== top50 - RF ======
Reference
Prediction Grade3 Normal Grade2 Grade1
Grade3 0 0 0 0
Normal 3 8 5 1
Grade2 0 1 0 1
Grade1 0 0 0 0
Test Accuracy: 0.421 | Kappa: -0.056 | Macro F1: 0.615 | Precision: 0.235 | Recall: 0.222 | Sensitivity: 0.222 | Specificity: 0.739 | Balanced Accuracy: 0.481
====== top80 - SVM ======
Reference
Prediction Grade3 Normal Grade2 Grade1
Grade3 1 0 0 1
Normal 2 8 5 0
Grade2 0 1 0 1
Grade1 0 0 0 0
Test Accuracy: 0.474 | Kappa: 0.095 | Macro F1: 0.533 | Precision: 0.344 | Recall: 0.306 | Sensitivity: 0.306 | Specificity: 0.774 | Balanced Accuracy: 0.54
====== top80 - KNN ======
Reference
Prediction Grade3 Normal Grade2 Grade1
Grade3 0 0 0 1
Normal 0 9 5 0
Grade2 2 0 0 1
Grade1 1 0 0 0
Test Accuracy: 0.474 | Kappa: 0.116 | Macro F1: 0.783 | Precision: 0.161 | Recall: 0.25 | Sensitivity: 0.25 | Specificity: 0.791 | Balanced Accuracy: 0.521
====== top80 - RF ======
Reference
Prediction Grade3 Normal Grade2 Grade1
Grade3 0 0 0 0
Normal 1 9 4 1
Grade2 2 0 1 1
Grade1 0 0 0 0
Test Accuracy: 0.526 | Kappa: 0.17 | Macro F1: 0.486 | Precision: 0.425 | Recall: 0.3 | Sensitivity: 0.3 | Specificity: 0.796 | Balanced Accuracy: 0.548
====== top110 - SVM ======
Reference
Prediction Grade3 Normal Grade2 Grade1
Grade3 2 0 2 1
Normal 1 9 3 0
Grade2 0 0 0 1
Grade1 0 0 0 0
Test Accuracy: 0.579 | Kappa: 0.321 | Macro F1: 0.659 | Precision: 0.364 | Recall: 0.417 | Sensitivity: 0.417 | Specificity: 0.835 | Balanced Accuracy: 0.626
====== top110 - KNN ======
Reference
Prediction Grade3 Normal Grade2 Grade1
Grade3 0 1 0 0
Normal 1 8 4 1
Grade2 1 0 1 1
Grade1 1 0 0 0
Test Accuracy: 0.474 | Kappa: 0.116 | Macro F1: 0.473 | Precision: 0.226 | Recall: 0.272 | Sensitivity: 0.272 | Specificity: 0.784 | Balanced Accuracy: 0.528
====== top110 - RF ======
Reference
Prediction Grade3 Normal Grade2 Grade1
Grade3 0 0 0 0
Normal 1 9 4 2
Grade2 2 0 1 0
Grade1 0 0 0 0
Test Accuracy: 0.526 | Kappa: 0.153 | Macro F1: 0.485 | Precision: 0.448 | Recall: 0.3 | Sensitivity: 0.3 | Specificity: 0.789 | Balanced Accuracy: 0.545 Code
acc_df <- do.call(rbind, acc_list)
summary_df <- do.call(rbind, summary_list)
desired_order <- c("top30", "top50", "top80", "top110")
acc_df$GeneSet <- factor(acc_df$GeneSet, levels = desired_order)
summary_df$GeneSet <- factor(summary_df$GeneSet, levels = desired_order)
# -------- Plot the CV accuracy boxplot --------
gse15852_cv_plot <- ggplot(acc_df, aes(x = GeneSet, y = Accuracy, fill = Model)) +
geom_boxplot(position = position_dodge(0.8), outlier.shape = NA) +
geom_jitter(position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8), size = 1, alpha = 0.6) +
labs(
title = "Model Accuracy (Linear selection)",
x = "Top Gene Set",
y = "Cross-validated Accuracy"
) +
theme_minimal(base_size = 13) +
theme(legend.position = "top")
# -------- Plot the Test evaluation metric bar chart --------
eval_long_gse15852 <- pivot_longer(summary_df,
cols = c("Accuracy", "MacroF1", "MacroPrecision", "MacroRecall", "MacroSensitivity", "MacroSpecificity", "BalancedAccuracy", "Kappa"),
names_to = "Metric", values_to = "Value")
gse15852_eval_plot <- ggplot(eval_long_gse15852 , aes(x = GeneSet, y = Value, fill = Model)) +
geom_col(position = position_dodge(0.8)) +
facet_wrap(~ Metric, scales = "free_y") +
labs(
title = "Test Set Evaluation: Extended Metrics",
x = "Top Gene Set",
y = "Metric"
) +
theme_minimal(base_size = 13) +
theme(legend.position = "top")
gse15852_cv_plot
Code
gse15852_eval_plot
8.1.5 dataframe of result for gse15852
Code
gse15852_metrics<- tidyr::pivot_wider(
eval_long_gse15852 ,
names_from = Metric,
values_from = Value
)
gse15852_metrics# A tibble: 12 × 10
GeneSet Model Accuracy MacroF1 MacroPrecision MacroRecall MacroSensitivity
<fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 top30 SVM 0.474 0.643 0.474 0.25 0.25
2 top30 KNN 0.526 0.61 0.521 0.333 0.333
3 top30 RF 0.526 0.556 0.517 0.322 0.322
4 top50 SVM 0.474 0.667 0.25 0.25 0.25
5 top50 KNN 0.421 0.615 0.157 0.222 0.222
6 top50 RF 0.421 0.615 0.235 0.222 0.222
7 top80 SVM 0.474 0.533 0.344 0.306 0.306
8 top80 KNN 0.474 0.783 0.161 0.25 0.25
9 top80 RF 0.526 0.486 0.425 0.3 0.3
10 top110 SVM 0.579 0.659 0.364 0.417 0.417
11 top110 KNN 0.474 0.473 0.226 0.272 0.272
12 top110 RF 0.526 0.485 0.448 0.3 0.3
# ℹ 3 more variables: MacroSpecificity <dbl>, BalancedAccuracy <dbl>,
# Kappa <dbl>Code
gse15852_metrics$Config <- paste(gse15852_metrics$Model, gse15852_metrics$GeneSet, sep = "_")
# Select top 5 models by Balanced Accuracy
top5 <- gse15852_metrics %>%
arrange(desc(BalancedAccuracy)) %>%
head(5)
# Reshape to long format for heatmap
top5_long <- melt(top5, id.vars = c("Model", "GeneSet", "Config"))
# Plot heatmap
gse15852_heat <- ggplot(top5_long, aes(x = variable, y = Config, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient(low = "white", high = "#d07095", name = "Metric Value") +
labs(title = "Performance Heatmap of Top 5 Models",
x = "Metric",
y = "Model (Config)") +
theme_minimal(base_size = 14) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
gse15852_heat
8.1.6 boxplot
Code
library(ggplot2)
library(dplyr)
library(tidyr)
combine_metrics$Dataset <- "Combined"
gse17907_metrics$Dataset <- "GSE17907"
gse15852_metrics$Dataset <- "GSE15852"
all_metrics <- bind_rows(combine_metrics, gse17907_metrics, gse15852_metrics)
metrics_long <- all_metrics %>%
dplyr::select(Dataset, Model, GeneSet, BalancedAccuracy, MacroF1) %>%
pivot_longer(cols = c("BalancedAccuracy", "MacroF1"),
names_to = "Metric", values_to = "Value")
boxplot_plot <- ggplot(metrics_long, aes(x = Model, y = Value, fill = Dataset)) +
geom_boxplot(outlier.shape = NA, alpha = 0.7, position = position_dodge(0.8)) +
facet_wrap(~ Metric, scales = "free_y") +
labs(title = "Model Comparison",
y = "Metric Value",
x = "Model") +
theme_minimal(base_size = 14) +
theme(
legend.position = "top",
plot.title = element_text(hjust = 0.5)
) +
scale_fill_manual(values = c("Combined" = "#F8AFA6",
"GSE17907" = "#c8b0d8",
"GSE15852" = "#d07095"))
boxplot_plot
8.2 Appendix B: Images of Shiny app
Code
knitr::include_graphics(list.files("shinypng", pattern = "*.png", full.names = TRUE))
